Add MCP text processor server

app.py

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+import gradio as gr
+import json
+
+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.")
+    
+    with gr.Tab("Analyze Text"):
+        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
+        )
+    
+    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)
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

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+gradio[mcp]
+mcp

server.py

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+from mcp.server.fastmcp import FastMCP
+import json
+
+mcp = FastMCP("text-processor")
+
+@mcp.tool()
+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,
+        "unique_words": len(set(word.lower() for word in words))
+    })
+
+@mcp.tool()
+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
+    """
+    # Remove common words
+    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]
+    })
+
+@mcp.tool()
+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())
+    syllables = text.count("a") + text.count("e") + text.count("i") + text.count("o") + text.count("u")
+    
+    if words == 0:
+        return json.dumps({"error": "No text to analyze"})
+    
+    # Flesch Kincaid Grade
+    grade = (0.39 * (words / sentences)) + (11.8 * (syllables / words)) - 15.59
+    grade = max(0, round(grade, 1))
+    
+    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": grade,
+        "reading_level": level
+    })
+
+@mcp.tool()
+def reverse_text(text: str) -> str:
+    """Reverse a string.
+    
+    Args:
+        text: The input text
+    
+    Returns:
+        The reversed text
+    """
+    return text[::-1]
+
+if __name__ == "__main__":
+    mcp.run()