Text Processor MCP

A text processing toolkit with 9 tools for text analysis, available as:

• MCP server (server.py) — for any MCP client (Claude, VS Code, etc.)
• Gradio web UI (app.py) — browser interface with tabs for each tool
• Pi plugin (text-processor-plugin/) — skills + extension for Pi coding agent

🚀 Quick Start

# Install dependencies
uv sync

# Run the MCP server (stdio)
uv run python server.py

# Run the Gradio web UI
uv run python app.py

🛠 Tools

#	Tool	Description	Method
1	Analyze Text	Word/sentence/character statistics	Pure Python
2	Extract Keywords	Most frequent words (with stopword filtering)	Counter
3	Check Reading Level	Flesch-Kincaid grade level estimate	Pure Python
4	Reverse Text	Reverse a string	Pure Python
5	Analyze Sentiment	Polarity (-1 to +1) and subjectivity	TextBlob
6	Detect Language	Identify language of text	langid
7	Summarize Text	Extractive summarization (word frequency scoring)	Pure Python
8	Check Spelling	Find misspelled words + suggest corrections	TextBlob
9	Readability Tips	Suggestions for clearer writing (sentence length, passive voice, repetition)	Pure Python

🧪 Examples

All examples use the same input text about English articles.

Input text:

The articles in English are the definite article the and the indefinite article a (which takes the alternate form an when followed by a vowel sound). They are the two most common determiners. The definite article is the default determiner when the speaker believes that the listener knows the identity of a common noun's referent (because it is obvious, because it is common knowledge, or because it was mentioned in the same sentence or an earlier sentence). The indefinite article is the default determiner for other singular, countable, common nouns, while no determiner is the default for other common nouns. Other determiners are used to add semantic information such as amount (many, a few), proximity (this, those), or possession (my, the government's).

---

1. Analyze Text

Input: analyze_text(text)

Output:

{
  "total_characters": 760,
  "characters_without_spaces": 639,
  "total_words": 122,
  "total_sentences": 5,
  "average_word_length": 5.24,
  "average_sentence_length": 24.4,
  "unique_words": 74
}

---

2. Extract Keywords

Input: extract_keywords(text, count=8)

Output:

{
  "keywords": [
    {"word": "common", "frequency": 5},
    {"word": "article", "frequency": 4},
    {"word": "default", "frequency": 3},
    {"word": "determiner", "frequency": 3},
    {"word": "other", "frequency": 3},
    {"word": "definite", "frequency": 2},
    {"word": "indefinite", "frequency": 2}
  ]
}

---

3. Check Reading Level

Input: check_reading_level(text)

Output:

{
  "grade_level": 17.6,
  "reading_level": "College/Academic"
}

---

4. Reverse Text

Input: reverse_text("Hello World")

Output:

dlroW olleH

---

5. Analyze Sentiment

Input: analyze_sentiment(text)

Output:

{
  "polarity": -0.034,
  "subjectivity": 0.388,
  "sentiment_label": "Neutral",
  "subjectivity_label": "Objective"
}

Polarity: -1 (negative) to +1 (positive). Subjectivity: 0 (objective) to 1 (subjective).

---

6. Detect Language

Input: detect_language(text)

Output:

{
  "language_code": "en",
  "language_name": "English",
  "confidence": -1851.642
}

More examples:

Input	Result
"Артикли в английском языке..."	ru (Russian)
"Les articles en anglais..."	fr (French)
"Die Artikel im Englischen..."	de (German)

---

7. Summarize Text

Input: summarize_text(text, max_sentences=3)

Output:

{
  "summary": "They are the two most common determiners. The indefinite article is the default determiner for other singular, countable, common nouns, while no determiner is the default for other common nouns.",
  "original_sentences": 5,
  "summary_sentences": 2,
  "compression_ratio": "40%"
}

---

8. Check Spelling

Input: check_spelling("The definte article is the most common determiner in english.")

Output:

{
  "misspelled_count": 2,
  "misspelled_words": [
    {
      "original": "definte",
      "suggestions": ["definite", "define"],
      "best_correction": "definite",
      "confidence": 0.857
    },
    {
      "original": "determiner",
      "suggestions": ["determined", "determine"],
      "best_correction": "determined",
      "confidence": 0.577
    }
  ],
  "corrected_text": "The definite article is the most common determined in english."
}

---

9. Readability Tips

Input: readability_tips(text)

Output:

{
  "overall_assessment": "Found 3 area(s) for improvement (severity: moderate).",
  "statistics": {
    "sentence_count": 5,
    "word_count": 122,
    "avg_sentence_length": 24.4,
    "avg_word_length": 5.0
  },
  "tips": [
    {
      "issue": "Sentences are slightly long",
      "detail": "Average sentence length is 24.4 words.",
      "suggestion": "Consider shortening some sentences to improve clarity."
    },
    {
      "issue": "Too many long words",
      "detail": "Found 4 words longer than 10 characters.",
      "suggestion": "Use simpler, shorter alternatives for complex terms."
    },
    {
      "issue": "Word repetition",
      "detail": "Some words are overused: \"common\" (5x), \"article\" (4x).",
      "suggestion": "Use synonyms or pronouns to vary your vocabulary."
    }
  ]
}

📦 Dependencies

• mcp[cli] — Model Context Protocol framework
• gradio — Web UI framework
• textblob — NLP (sentiment, spelling)
• langid — Language detection (97 languages)

🏗 Project Structure

text-processor-mcp/
├── .mcp.json                 # MCP client config (stdio)
├── .gitignore                # Git ignore rules
├── server.py                 # MCP server with all 9 tools
├── app.py                    # Gradio web UI (8 tabs)
├── pyproject.toml            # Project config & dependencies
├── uv.lock                   # Locked dependency versions
├── text-processor-plugin/    # Pi package (skills + extension)
│   ├── package.json
│   ├── README.md
│   ├── extensions/
│   │   └── text-processor.ts
│   └── skills/
│       ├── analyze-text/SKILL.md
│       ├── extract-keywords/SKILL.md
│       ├── check-reading-level/SKILL.md
│       ├── reverse-text/SKILL.md
│       ├── analyze-sentiment/SKILL.md
│       ├── detect-language/SKILL.md
│       ├── summarize-text/SKILL.md
│       ├── check-spelling/SKILL.md
│       └── readability-tips/SKILL.md
└── README.md

🔌 MCP Integration

Two ways to connect: stdio (standalone MCP server) or SSE (via Gradio, which also serves the web UI).

Choose the approach that fits your client.

---

⚡ Pi (the coding agent)

Pi doesn't support MCP natively. Two options:

Option 1 — Use the Pi plugin (recommended):

Install the plugin with skills + extension:

pi install ./text-processor-plugin

Then install the MCP adapter for direct tool access:

pi install npm:pi-mcp-adapter

The project already ships with .mcp.json pre-configured for stdio.

Option 2 — Global config via ~/.config/pi/config.json:

{
  "mcpServers": {
    "text-processor": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/text-processor-mcp", "python", "server.py"]
    }
  }
}

---

🖥 Claude Desktop

In claude_desktop_config.json:

{
  "mcpServers": {
    "text-processor": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/text-processor-mcp", "python", "server.py"]
    }
  }
}

---

💻 VS Code (Cline / Continue / etc.)

These clients read .mcp.json from the project root automatically:

{
  "servers": {
    "text-processor": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--directory", "/path/to/text-processor-mcp", "python", "server.py"]
    }
  }
}

Note: Replace /path/to/text-processor-mcp with the absolute path to the project.

---

📁 Project-level .mcp.json (already included)

The project ships with .mcp.json pre-configured for stdio (standalone MCP server). Edit it to switch to SSE or adjust paths as needed.

🖥 Web UI

Run uv run python app.py to open the Gradio interface with tabs for each tool. The Gradio app also exposes MCP tools via SSE at http://127.0.0.1:7860/gradio_api/mcp/.