File size: 3,031 Bytes
d3697b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | 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() |