logs

.gitignore

@@ -0,0 +1,11 @@
+__pycache__/
+*.py[cod]
+.pytest_cache/
+.venv*/
+.env
+.env.*
+!.env.example
+!.env.local.example
+!.env.production.example
+config/auth.yaml
+surveys/*/*.csv

app.py

@@ -1,119 +1,114 @@
-import gradio as gr
 import json
+import logging
+from datetime import datetime, timezone
+from logging.handlers import RotatingFileHandler
+from pathlib import Path
+
+import gradio as gr
+
+from text_tools import (
+    analyze_sentiment,
+    analyze_text,
+    check_reading_level,
+    detect_language,
+    extract_keywords,
+    readability_advice,
+    spell_check,
+    summarize_text,
+)
+
+
+LOG_DIR = Path("logs")
+LOG_FILE = LOG_DIR / "mcp.log"
+
+
+def _utc_now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class JsonLineFormatter(logging.Formatter):
+    def format(self, record):
+        message = record.getMessage()
+        try:
+            payload = json.loads(message)
+            payload["level"] = record.levelname
+            payload["logger"] = record.name
+            return json.dumps(payload, ensure_ascii=False)
+        except json.JSONDecodeError:
+            return json.dumps({
+                "timestamp": _utc_now_iso(),
+                "level": record.levelname,
+                "logger": record.name,
+                "message": message,
+            }, ensure_ascii=False)
+
+
+def _configure_logging() -> None:
+    LOG_DIR.mkdir(parents=True, exist_ok=True)
+    root_logger = logging.getLogger()
+    root_logger.setLevel(logging.INFO)
+
+    text_formatter = logging.Formatter("%(asctime)s %(levelname)s %(name)s %(message)s")
+
+    stream_handler = logging.StreamHandler()
+    stream_handler.setFormatter(text_formatter)
+    root_logger.addHandler(stream_handler)
+
+    file_handler = RotatingFileHandler(LOG_FILE, maxBytes=1_000_000, backupCount=3)
+    file_handler.setFormatter(JsonLineFormatter())
+    root_logger.addHandler(file_handler)
+
 
-def analyze_text(text: str) -> str:
-    """Analyze text and return statistics.
-    
-    Args:
-        text: The input text to analyze
-    
-    Returns:
-        JSON string with analysis results
-    """
-    words = text.split()
-    chars = len(text)
-    chars_no_spaces = len(text.replace(" ", ""))
-    sentences = text.count(".") + text.count("!") + text.count("?")
-    
-    avg_word_length = round(chars_no_spaces / len(words), 2) if words else 0
-    avg_sentence_length = round(len(words) / max(sentences, 1), 2)
-    
-    return json.dumps({
-        "total_characters": chars,
-        "characters_without_spaces": chars_no_spaces,
-        "total_words": len(words),
-        "total_sentences": max(sentences, 1),
-        "average_word_length": avg_word_length,
-        "average_sentence_length": avg_sentence_length
-    }, indent=2)
-
-def extract_keywords(text: str, count: int = 5) -> str:
-    """Extract keywords (most common words) from text.
-    
-    Args:
-        text: The input text
-        count: Number of keywords to return (default 5)
-    
-    Returns:
-        JSON string with keywords and frequencies
-    """
-    stopwords = {
-        "the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for",
-        "of", "with", "is", "are", "was", "were", "be", "been", "by", "from"
-    }
-    
-    words = text.lower().split()
-    filtered = [w.strip(".,!?;:") for w in words if w.lower() not in stopwords]
-    
-    from collections import Counter
-    word_freq = Counter(filtered)
-    top_words = word_freq.most_common(count)
-    
-    return json.dumps({
-        "keywords": [{"word": w, "frequency": f} for w, f in top_words]
-    }, indent=2)
-
-def check_reading_level(text: str) -> str:
-    """Estimate reading difficulty level.
-    
-    Args:
-        text: The input text
-    
-    Returns:
-        JSON string with reading level estimate
-    """
-    sentences = max(text.count(".") + text.count("!") + text.count("?"), 1)
-    words = len(text.split())
-    vowels = "aeiou"
-    syllables = sum(1 for c in text.lower() if c in vowels)
-    
-    if words == 0:
-        return json.dumps({"error": "No text to analyze"})
-    
-    grade = max(0, (0.39 * (words / sentences)) + (11.8 * (syllables / words)) - 15.59)
-    
-    if grade < 6:
-        level = "Elementary School"
-    elif grade < 9:
-        level = "Middle School"
-    elif grade < 13:
-        level = "High School"
-    else:
-        level = "College/Academic"
-    
-    return json.dumps({
-        "grade_level": round(grade, 1),
-        "reading_level": level
-    }, indent=2)
-
-# Create web UI
 with gr.Blocks(title="Text Processor") as demo:
     gr.Markdown("# Text Processing Tools")
-    gr.Markdown("Analyze text statistics, extract keywords, and check reading difficulty.")
-    
+    gr.Markdown(
+        "Analyze text statistics, extract keywords, check reading difficulty, "
+        "detect sentiment, identify language, summarize text, check spelling, "
+        "and suggest readability improvements."
+    )
+
     with gr.Tab("Analyze Text"):
-        text_input1 = gr.Textbox(
-            label="Enter text",
-            lines=8,
-            placeholder="Paste your text here..."
-        )
+        text_input1 = gr.Textbox(label="Enter text", lines=8, placeholder="Paste your text here...")
         analysis_output = gr.Textbox(label="Analysis Results", lines=8)
         gr.Button("Analyze", size="lg").click(analyze_text, text_input1, analysis_output)
-    
+
     with gr.Tab("Extract Keywords"):
         text_input2 = gr.Textbox(label="Enter text", lines=8)
         count_input = gr.Slider(1, 20, value=5, step=1, label="Number of keywords")
         keywords_output = gr.Textbox(label="Keywords", lines=8)
-        gr.Button("Extract", size="lg").click(
-            extract_keywords,
-            [text_input2, count_input],
-            keywords_output
-        )
-    
+        gr.Button("Extract", size="lg").click(extract_keywords, [text_input2, count_input], keywords_output)
+
     with gr.Tab("Reading Level"):
         text_input3 = gr.Textbox(label="Enter text", lines=8)
         level_output = gr.Textbox(label="Reading Level Analysis", lines=5)
         gr.Button("Check Level", size="lg").click(check_reading_level, text_input3, level_output)
 
+    with gr.Tab("Sentiment"):
+        text_input4 = gr.Textbox(label="Enter text", lines=8)
+        sentiment_output = gr.Textbox(label="Sentiment Analysis", lines=6)
+        gr.Button("Analyze Sentiment", size="lg").click(analyze_sentiment, text_input4, sentiment_output)
+
+    with gr.Tab("Language"):
+        text_input5 = gr.Textbox(label="Enter text", lines=8)
+        language_output = gr.Textbox(label="Language Detection", lines=8)
+        gr.Button("Detect Language", size="lg").click(detect_language, text_input5, language_output)
+
+    with gr.Tab("Summary"):
+        text_input6 = gr.Textbox(label="Enter text", lines=8)
+        summary_output = gr.Textbox(label="Summary", lines=8)
+        gr.Button("Summarize", size="lg").click(summarize_text, text_input6, summary_output)
+
+    with gr.Tab("Spell Check"):
+        text_input7 = gr.Textbox(label="Enter text", lines=8)
+        spell_output = gr.Textbox(label="Spelling Analysis", lines=10)
+        gr.Button("Check Spelling", size="lg").click(spell_check, text_input7, spell_output)
+
+    with gr.Tab("Readability Tips"):
+        text_input8 = gr.Textbox(label="Enter text", lines=8)
+        tips_output = gr.Textbox(label="Readability Advice", lines=10)
+        gr.Button("Get Advice", size="lg").click(readability_advice, text_input8, tips_output)
+
+
 if __name__ == "__main__":
-    demo.launch(mcp_server=True)
+    _configure_logging()
+    demo.launch(mcp_server=True)

logs/mcp.log

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+{"timestamp": "2026-05-26T13:51:02.642299+00:00", "tool": "analyze_text", "event": "completed", "status": "success", "duration_ms": 0.11, "chars": 313, "words": 55, "lines": 1, "result_chars": 176, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:51:02.663240+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=b337fb51-848e-4e7b-87c1-439838f89846 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:51:03.202193+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:51:07.296204+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:51:07.312900+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:51:07.316730+00:00", "tool": "extract_keywords", "event": "completed", "status": "success", "duration_ms": 0.29, "chars": 313, "words": 55, "lines": 1, "result_chars": 576, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:51:07.322468+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=b337fb51-848e-4e7b-87c1-439838f89846 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:51:07.871150+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:51:10.772131+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:51:10.782465+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:51:10.782130+00:00", "tool": "check_reading_level", "event": "completed", "status": "success", "duration_ms": 0.08, "chars": 313, "words": 55, "lines": 1, "result_chars": 64, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:51:10.795252+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=b337fb51-848e-4e7b-87c1-439838f89846 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:51:11.342098+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:52:26.623087+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: HEAD https://huggingface.co/api/telemetry/https%3A/api.gradio.app/gradio-initiated-analytics \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:52:26.692241+00:00", "level": "INFO", "logger": "mcp.server.streamable_http_manager", "message": "StreamableHTTP session manager started"}
+{"timestamp": "2026-05-26T13:52:26.727603+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/startup-events \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:52:26.764866+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: HEAD http://127.0.0.1:7860/ \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:52:26.899545+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: HEAD https://huggingface.co/api/telemetry/https%3A/api.gradio.app/gradio-launched-telemetry \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:52:27.223868+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET https://api.gradio.app/pkg-version \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:53:52.708442+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:53:52.750211+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:53:52.766435+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type ListToolsRequest"}
+{"timestamp": "2026-05-26T13:53:52.769355+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:53:53.189922+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type ListResourcesRequest"}
+{"timestamp": "2026-05-26T13:53:53.191028+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:53:53.206160+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type ListResourceTemplatesRequest"}
+{"timestamp": "2026-05-26T13:53:53.207297+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:57:01.096515+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:57:01.128081+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/config \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:01.139655+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/info?serialize=False \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:01.154258+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/heartbeat/f0251319-219e-46f9-b220-1caa6c21a9c9 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:01.163872+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:01.163397+00:00", "tool": "analyze_text", "event": "completed", "status": "success", "duration_ms": 0.08, "chars": 315, "words": 55, "lines": 3, "result_chars": 176, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:57:01.178951+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=f0251319-219e-46f9-b220-1caa6c21a9c9 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:01.343089+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: HEAD https://huggingface.co/api/telemetry/py_client/initiated \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:01.725157+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:57:36.045952+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:57:36.053904+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:36.055872+00:00", "tool": "extract_keywords", "event": "completed", "status": "success", "duration_ms": 0.13, "chars": 315, "words": 55, "lines": 3, "result_chars": 577, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:57:36.065960+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=f0251319-219e-46f9-b220-1caa6c21a9c9 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:36.612140+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:57:38.825342+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:57:38.833412+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:38.833612+00:00", "tool": "check_reading_level", "event": "completed", "status": "success", "duration_ms": 0.08, "chars": 315, "words": 55, "lines": 3, "result_chars": 64, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:57:38.844484+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=f0251319-219e-46f9-b220-1caa6c21a9c9 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:57:39.389627+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:58:12.369874+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:58:12.380988+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:58:12.380679+00:00", "tool": "analyze_text", "event": "completed", "status": "success", "duration_ms": 0.06, "chars": 315, "words": 55, "lines": 3, "result_chars": 176, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:58:12.394994+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=f0251319-219e-46f9-b220-1caa6c21a9c9 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:58:12.939824+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:58:28.559871+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:58:28.569748+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:58:28.572372+00:00", "tool": "extract_keywords", "event": "completed", "status": "success", "duration_ms": 0.22, "chars": 315, "words": 55, "lines": 3, "result_chars": 577, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:58:28.588689+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=f0251319-219e-46f9-b220-1caa6c21a9c9 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:58:29.127947+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}
+{"timestamp": "2026-05-26T13:58:38.412009+00:00", "level": "INFO", "logger": "mcp.server.lowlevel.server", "message": "Processing request of type CallToolRequest"}
+{"timestamp": "2026-05-26T13:58:38.417847+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: POST http://127.0.0.1:7860/gradio_api/queue/join \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:58:38.421093+00:00", "tool": "check_reading_level", "event": "completed", "status": "success", "duration_ms": 0.06, "chars": 315, "words": 55, "lines": 3, "result_chars": 64, "level": "INFO", "logger": "text_tools"}
+{"timestamp": "2026-05-26T13:58:38.427623+00:00", "level": "INFO", "logger": "httpx", "message": "HTTP Request: GET http://127.0.0.1:7860/gradio_api/queue/data?session_hash=f0251319-219e-46f9-b220-1caa6c21a9c9 \"HTTP/1.1 200 OK\""}
+{"timestamp": "2026-05-26T13:58:38.975364+00:00", "level": "INFO", "logger": "mcp.server.streamable_http", "message": "Terminating session: None"}

text_tools.py

@@ -0,0 +1,472 @@
+import json
+import logging
+import re
+import time
+import unicodedata
+from collections import Counter
+from datetime import datetime, timezone
+from difflib import get_close_matches
+from functools import wraps
+
+
+logger = logging.getLogger(__name__)
+
+POSITIVE_WORDS = {
+    "alegre", "amor", "bueno", "buen", "bonito", "brillante", "calma", "feliz",
+    "genial", "grande", "hermoso", "ideal", "increible", "maravilloso", "mejor",
+    "optimista", "paz", "positivo", "sano", "satisfecho", "triunfo", "victoria",
+}
+NEGATIVE_WORDS = {
+    "odio", "malo", "mal", "triste", "angustia", "asco", "depresion", "dolor",
+    "enfermo", "fracaso", "furia", "horrible", "miedo", "negativo", "pesimo",
+    "rabia", "sufrimiento", "terrible", "vacio", "desamor", "desesperacion",
+}
+LANGUAGE_STOPWORDS = {
+    "es": {
+        "el", "la", "los", "las", "de", "del", "y", "que", "en", "un", "una", "por",
+        "con", "para", "no", "se", "es", "al", "lo", "como", "mas", "pero",
+        "sus", "ya", "o", "si", "entre", "cuando", "muy", "sin", "sobre", "me", "mi",
+    },
+    "en": {
+        "the", "and", "to", "of", "a", "in", "is", "it", "you", "that", "he", "was",
+        "for", "on", "are", "with", "as", "i", "his", "they", "be", "at", "one", "have",
+    },
+    "fr": {
+        "le", "la", "les", "de", "des", "et", "un", "une", "en", "que", "est", "pour",
+        "dans", "pas", "qui", "du", "au", "sur", "ce", "il", "elle", "nous", "vous",
+    },
+    "pt": {
+        "o", "a", "os", "as", "de", "do", "da", "e", "em", "que", "um", "uma", "para",
+        "com", "nao", "por", "como", "mais", "mas", "se", "dos", "das", "no",
+    },
+    "it": {
+        "il", "la", "lo", "gli", "le", "di", "e", "che", "un", "una", "per", "con",
+        "non", "come", "piu", "su", "del", "della", "nel", "nei", "da", "al",
+    },
+}
+
+SPANISH_COMMON_WORDS = {
+    "agua", "ahora", "aire", "alguien", "algo", "amigo", "amor", "antes", "aqui",
+    "aun", "bajo", "bastante", "bien", "cabe", "cada", "camino", "casa", "cerca",
+    "ciudad", "claro", "comer", "cosa", "cuerpo", "dia", "diez", "donde", "dueno",
+    "ejemplo", "entonces", "escribir", "escuela", "este", "familia", "feliz", "fuerza",
+    "gente", "grande", "hablar", "hacer", "hombre", "hoy", "idea", "jugar", "lugar",
+    "mano", "mar", "mientras", "mundo", "nada", "niño", "noches", "nombre", "noche",
+    "octubre", "ojo", "pais", "palabra", "paz", "persona", "problema", "puede", "pueblo",
+    "querer", "razon", "salir", "saber", "sala", "sierra", "soria", "tarde", "tiempo",
+    "tierra", "trabajo", "todo", "trigo", "ultramar", "vida", "viaje", "viajero", "voz",
+}
+ENGLISH_COMMON_WORDS = {
+    "about", "after", "again", "air", "all", "also", "am", "an", "and", "any", "are",
+    "back", "be", "because", "before", "big", "book", "but", "by", "can", "come",
+    "day", "do", "down", "each", "end", "even", "family", "for", "from", "give",
+    "good", "great", "had", "has", "have", "he", "her", "here", "his", "home", "house",
+    "how", "i", "if", "in", "into", "it", "its", "just", "know", "like", "little",
+    "look", "make", "man", "many", "me", "more", "most", "my", "new", "no", "not",
+    "now", "one", "or", "our", "out", "people", "place", "said", "see", "she", "so",
+    "some", "time", "to", "two", "up", "us", "very", "was", "we", "well", "what",
+    "when", "where", "which", "who", "why", "will", "with", "work", "world", "you",
+}
+
+
+def _summarize_text(text: str) -> dict:
+    words = text.split()
+    return {
+        "chars": len(text),
+        "words": len(words),
+        "lines": text.count("\n") + 1 if text else 0,
+    }
+
+
+def _normalize_word(word: str) -> str:
+    word = word.lower().strip()
+    word = re.sub(r"^[^\wáéíóúüñ]+|[^\wáéíóúüñ]+$", "", word, flags=re.UNICODE)
+    normalized = unicodedata.normalize("NFD", word)
+    return "".join(char for char in normalized if unicodedata.category(char) != "Mn")
+
+
+def _normalize_words_from_source(words: set[str]) -> set[str]:
+    return {_normalize_word(word) for word in words}
+
+
+SPELLING_LEXICONS = {
+    "es": (
+        _normalize_words_from_source(LANGUAGE_STOPWORDS["es"])
+        | _normalize_words_from_source(SPANISH_COMMON_WORDS)
+        | _normalize_words_from_source(POSITIVE_WORDS)
+        | _normalize_words_from_source(NEGATIVE_WORDS)
+    ),
+    "en": (
+        _normalize_words_from_source(LANGUAGE_STOPWORDS["en"])
+        | _normalize_words_from_source(ENGLISH_COMMON_WORDS)
+        | _normalize_words_from_source(POSITIVE_WORDS)
+        | _normalize_words_from_source(NEGATIVE_WORDS)
+    ),
+}
+
+
+def _utc_now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _json_log(level: int, payload: dict) -> None:
+    logger.log(level, json.dumps(payload, ensure_ascii=False, separators=(",", ":")))
+
+
+def _log_tool(name: str):
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            metadata = {}
+            if args and isinstance(args[0], str):
+                metadata.update(_summarize_text(args[0]))
+            if "count" in kwargs:
+                metadata["count"] = kwargs["count"]
+
+            start = time.perf_counter()
+            try:
+                result = func(*args, **kwargs)
+                duration_ms = round((time.perf_counter() - start) * 1000, 2)
+                _json_log(logging.INFO, {
+                    "timestamp": _utc_now_iso(),
+                    "tool": name,
+                    "event": "completed",
+                    "status": "success",
+                    "duration_ms": duration_ms,
+                    **metadata,
+                    "result_chars": len(result) if isinstance(result, str) else None,
+                })
+                return result
+            except Exception:
+                duration_ms = round((time.perf_counter() - start) * 1000, 2)
+                _json_log(logging.ERROR, {
+                    "timestamp": _utc_now_iso(),
+                    "tool": name,
+                    "event": "completed",
+                    "status": "error",
+                    "duration_ms": duration_ms,
+                    **metadata,
+                })
+                raise
+
+        return wrapper
+
+    return decorator
+
+
+def _split_sentences(text: str) -> list[str]:
+    cleaned = text.strip()
+    if not cleaned:
+        return []
+    sentences = re.split(r"(?<=[.!?])\s+", cleaned)
+    return [sentence.strip() for sentence in sentences if sentence.strip()]
+
+
+def _extract_tokens(text: str) -> list[str]:
+    return [token for token in re.findall(r"[A-Za-zÁÉÍÓÚÜÑáéíóúüñ]+", text)]
+
+
+def _language_info(text: str) -> dict:
+    tokens = [_normalize_word(token) for token in _extract_tokens(text)]
+    tokens = [token for token in tokens if token]
+    token_set = set(tokens)
+
+    scores = {}
+    evidence = {}
+    accent_signals = {
+        "es": {"ñ", "á", "é", "í", "ó", "ú", "¿", "¡"},
+        "fr": {"à", "â", "ç", "é", "è", "ê", "ë", "î", "ï", "ô", "ù", "û", "ü"},
+        "pt": {"ã", "ç", "á", "à", "â", "é", "ê", "í", "ó", "ô", "õ", "ú"},
+        "it": {"à", "è", "é", "ì", "ò", "ù"},
+    }
+
+    lowered_text = text.lower()
+    for language, stopwords in LANGUAGE_STOPWORDS.items():
+        stopword_hits = sum(1 for token in token_set if token in stopwords)
+        accent_hits = sum(1 for char in lowered_text if char in accent_signals.get(language, set()))
+        scores[language] = stopword_hits + (accent_hits * 0.5)
+        evidence[language] = {
+            "stopword_hits": stopword_hits,
+            "accent_hits": accent_hits,
+        }
+
+    best_language = max(scores, key=scores.get) if scores else "unknown"
+    best_score = scores.get(best_language, 0)
+    second_best = sorted(scores.values(), reverse=True)[1] if len(scores) > 1 else 0
+    confidence = round(((best_score - second_best) / max(best_score, 1)) * 100, 1) if best_score else 0.0
+
+    if best_score == 0:
+        detected = "unknown"
+        confidence = 0.0
+    else:
+        detected = best_language
+
+    return {
+        "language": detected,
+        "confidence": confidence,
+        "scores": scores,
+        "evidence": evidence,
+        "token_count": len(tokens),
+        "tokens": tokens,
+    }
+
+
+def _sentence_stopwords(language: str) -> set[str]:
+    return LANGUAGE_STOPWORDS.get(language, LANGUAGE_STOPWORDS["es"])
+
+
+def _spell_lexicon(language: str) -> set[str]:
+    if language in SPELLING_LEXICONS:
+        return SPELLING_LEXICONS[language]
+    return SPELLING_LEXICONS["es"] | SPELLING_LEXICONS["en"]
+
+
+def _clean_result_sentences(sentences: list[str]) -> list[str]:
+    return [sentence.strip() for sentence in sentences if sentence.strip()]
+
+
+@_log_tool("analyze_text")
+def analyze_text(text: str) -> str:
+    words = text.split()
+    chars = len(text)
+    chars_no_spaces = len(text.replace(" ", ""))
+    sentences = text.count(".") + text.count("!") + text.count("?")
+
+    avg_word_length = round(chars_no_spaces / len(words), 2) if words else 0
+    avg_sentence_length = round(len(words) / max(sentences, 1), 2)
+
+    return json.dumps({
+        "total_characters": chars,
+        "characters_without_spaces": chars_no_spaces,
+        "total_words": len(words),
+        "total_sentences": max(sentences, 1),
+        "average_word_length": avg_word_length,
+        "average_sentence_length": avg_sentence_length,
+    }, indent=2, ensure_ascii=False)
+
+
+@_log_tool("extract_keywords")
+def extract_keywords(text: str, count: int = 5) -> str:
+    stopwords = {
+        "the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for",
+        "of", "with", "is", "are", "was", "were", "be", "been", "by", "from",
+    }
+
+    words = text.lower().split()
+    filtered = [w.strip(".,!?;:") for w in words if w.lower() not in stopwords]
+
+    word_freq = Counter(filtered)
+    top_words = word_freq.most_common(count)
+
+    return json.dumps({
+        "keywords": [{"word": w, "frequency": f} for w, f in top_words]
+    }, indent=2, ensure_ascii=False)
+
+
+@_log_tool("check_reading_level")
+def check_reading_level(text: str) -> str:
+    sentences = max(text.count(".") + text.count("!") + text.count("?"), 1)
+    words = len(text.split())
+    vowels = "aeiou"
+    syllables = sum(1 for c in text.lower() if c in vowels)
+
+    if words == 0:
+        return json.dumps({"error": "No text to analyze"}, ensure_ascii=False)
+
+    grade = max(0, (0.39 * (words / sentences)) + (11.8 * (syllables / words)) - 15.59)
+
+    if grade < 6:
+        level = "Elementary School"
+    elif grade < 9:
+        level = "Middle School"
+    elif grade < 13:
+        level = "High School"
+    else:
+        level = "College/Academic"
+
+    return json.dumps({
+        "grade_level": round(grade, 1),
+        "reading_level": level,
+    }, indent=2, ensure_ascii=False)
+
+
+@_log_tool("analyze_sentiment")
+def analyze_sentiment(text: str) -> str:
+    tokens = [_normalize_word(token) for token in _extract_tokens(text)]
+    tokens = [token for token in tokens if token]
+
+    positive_hits = sum(1 for token in tokens if token in POSITIVE_WORDS)
+    negative_hits = sum(1 for token in tokens if token in NEGATIVE_WORDS)
+    total_hits = positive_hits + negative_hits
+
+    if total_hits == 0:
+        label = "neutral"
+        score = 0
+    else:
+        score = positive_hits - negative_hits
+        if score > 0:
+            label = "positive"
+        elif score < 0:
+            label = "negative"
+        else:
+            label = "neutral"
+
+    confidence = round((abs(score) / total_hits) * 100, 1) if total_hits else 0.0
+
+    return json.dumps({
+        "sentiment": label,
+        "score": score,
+        "confidence": confidence,
+        "positive_hits": positive_hits,
+        "negative_hits": negative_hits,
+        "total_scored_tokens": total_hits,
+    }, indent=2, ensure_ascii=False)
+
+
+@_log_tool("detect_language")
+def detect_language(text: str) -> str:
+    info = _language_info(text)
+    return json.dumps({
+        "language": info["language"],
+        "confidence": info["confidence"],
+        "scores": info["scores"],
+        "evidence": info["evidence"],
+        "token_count": info["token_count"],
+    }, indent=2, ensure_ascii=False)
+
+
+@_log_tool("summarize_text")
+def summarize_text(text: str) -> str:
+    sentences = _clean_result_sentences(_split_sentences(text))
+    if not sentences:
+        return json.dumps({"error": "No text to summarize"}, ensure_ascii=False)
+
+    language = _language_info(text)["language"]
+    stopwords = _sentence_stopwords(language)
+    tokens = [_normalize_word(token) for token in _extract_tokens(text)]
+    tokens = [token for token in tokens if token and token not in stopwords]
+
+    if len(sentences) <= 2:
+        summary = " ".join(sentences)
+        return json.dumps({
+            "summary": summary,
+            "method": "direct",
+            "source_sentences": len(sentences),
+            "selected_sentences": len(sentences),
+        }, indent=2, ensure_ascii=False)
+
+    frequency = Counter(tokens)
+    sentence_scores = []
+    for index, sentence in enumerate(sentences):
+        sentence_tokens = [_normalize_word(token) for token in _extract_tokens(sentence)]
+        sentence_tokens = [token for token in sentence_tokens if token and token not in stopwords]
+        if not sentence_tokens:
+            continue
+        score = sum(frequency[token] for token in sentence_tokens) / (len(sentence_tokens) ** 0.5)
+        sentence_scores.append((index, score, sentence))
+
+    if not sentence_scores:
+        summary_sentences = sentences[:2]
+    else:
+        max_sentences = 1 if len(sentences) < 4 else 2
+        selected_indexes = sorted(
+            index for index, _, _ in sorted(sentence_scores, key=lambda item: item[1], reverse=True)[:max_sentences]
+        )
+        summary_sentences = [sentences[index] for index in selected_indexes]
+
+    summary = " ".join(summary_sentences)
+    if len(summary) > 320:
+        summary = summary[:317].rsplit(" ", 1)[0] + "..."
+
+    return json.dumps({
+        "summary": summary,
+        "method": "extractive",
+        "source_sentences": len(sentences),
+        "selected_sentences": len(summary_sentences),
+    }, indent=2, ensure_ascii=False)
+
+
+@_log_tool("spell_check")
+def spell_check(text: str) -> str:
+    info = _language_info(text)
+    language = info["language"] if info["language"] in {"es", "en"} else "es"
+    lexicon = _spell_lexicon(language)
+
+    raw_words = re.findall(r"[A-Za-zÁÉÍÓÚÜÑáéíóúüñ]+", text)
+    misspellings = []
+
+    for raw_word in raw_words:
+        normalized = _normalize_word(raw_word)
+        if not normalized:
+            continue
+        if normalized in lexicon:
+            continue
+        if raw_word[:1].isupper() and raw_word[1:].islower():
+            continue
+        if len(normalized) <= 2:
+            continue
+
+        suggestions = get_close_matches(normalized, list(lexicon), n=3, cutoff=0.84)
+        misspellings.append({
+            "word": raw_word,
+            "normalized": normalized,
+            "suggestions": suggestions,
+        })
+
+    unique_misspellings = []
+    seen = set()
+    for item in misspellings:
+        key = item["normalized"]
+        if key in seen:
+            continue
+        seen.add(key)
+        unique_misspellings.append(item)
+
+    return json.dumps({
+        "language": language,
+        "misspelled_words": unique_misspellings,
+        "misspelled_count": len(unique_misspellings),
+        "dictionary_mode": "heuristic",
+        "note": "Heuristic spell checker; proper nouns and technical terms may require manual review.",
+    }, indent=2, ensure_ascii=False)
+
+
+@_log_tool("readability_advice")
+def readability_advice(text: str) -> str:
+    words = text.split()
+    sentences = _clean_result_sentences(_split_sentences(text))
+    sentence_count = max(len(sentences), 1)
+    word_count = len(words)
+    char_count = len(text)
+    avg_sentence_length = round(word_count / sentence_count, 2)
+    avg_word_length = round((len(text.replace(" ", "")) / word_count), 2) if word_count else 0
+    long_words = sum(1 for word in words if len(_normalize_word(word)) >= 12)
+    paragraphs = [paragraph for paragraph in text.split("\n\n") if paragraph.strip()]
+
+    advice = []
+    if avg_sentence_length > 24:
+        advice.append("Divide las oraciones largas para reducir la carga cognitiva.")
+    if avg_sentence_length > 35:
+        advice.append("Hay oraciones muy extensas; considera fragmentarlas en dos o más frases.")
+    if avg_word_length > 6:
+        advice.append("Sustituye palabras largas o rebuscadas por términos más directos cuando sea posible.")
+    if long_words / max(word_count, 1) > 0.2:
+        advice.append("Hay muchos términos largos; mezcla vocabulario complejo con palabras más simples.")
+    if word_count > 120 and len(paragraphs) <= 1:
+        advice.append("Se beneficiaría de párrafos más cortos y espacios visuales entre ideas.")
+    if sentence_count < 3 and word_count > 80:
+        advice.append("Intenta repartir el contenido en más frases para mejorar el ritmo de lectura.")
+    if not advice:
+        advice.append("La claridad general es buena; solo revisa si puedes simplificar alguna frase puntual.")
+
+    return json.dumps({
+        "metrics": {
+            "characters": char_count,
+            "words": word_count,
+            "sentences": sentence_count,
+            "average_sentence_length": avg_sentence_length,
+            "average_word_length": avg_word_length,
+            "long_word_ratio": round(long_words / max(word_count, 1), 3),
+        },
+        "advice": advice,
+    }, indent=2, ensure_ascii=False)