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| from mcp.server.fastmcp import FastMCP | |
| import json | |
| mcp = FastMCP("text-processor") | |
| 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)) | |
| }) | |
| 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] | |
| }) | |
| 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 | |
| }) | |
| 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() |