oyemi-mcp 0.1.0

MCP Server for Oyemi semantic lexicon - deterministic word-to-code mapping with valence analysis

Oyemi MCP Server

MCP (Model Context Protocol) server for the Oyemi semantic lexicon. Provides deterministic word-to-code mapping and valence analysis for AI agents like Claude, ChatGPT, and Gemini.

Features

• Semantic Encoding: Convert words to deterministic semantic codes
• Valence Analysis: Analyze text sentiment using lexicon-based valence
• Semantic Similarity: Measure how similar two words are
• Synonym/Antonym Lookup: Find related words
• Zero Runtime Dependencies: No external NLP libraries needed at runtime

Installation

pip install oyemi-mcp

Or install from source:

git clone https://github.com/Osseni94/oyemi-mcp
cd oyemi-mcp
pip install -e .

Configuration

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "oyemi": {
      "command": "oyemi-mcp"
    }
  }
}

Claude Code

Add to your MCP settings:

{
  "mcpServers": {
    "oyemi": {
      "command": "oyemi-mcp"
    }
  }
}

Available Tools

encode_word

Encode a word to its semantic code.

encode_word("happy")
-> {
    "word": "happy",
    "code": "1023-00012-3-2-1",
    "pos": "adjective",
    "abstractness": "abstract",
    "valence": "positive"
}

analyze_text

Analyze the valence/sentiment of text.

analyze_text("I feel hopeful but anxious about the future")
-> {
    "valence_score": 0.0,
    "sentiment": "neutral",
    "positive_words": ["hopeful"],
    "negative_words": ["anxious"],
    ...
}

semantic_similarity

Compare two words semantically.

semantic_similarity("happy", "joyful")
-> {
    "similarity": 0.85,
    "relationship": "very similar"
}

find_synonyms

Find synonyms for a word.

find_synonyms("happy")
-> {
    "synonyms": ["glad", "felicitous", "well-chosen"]
}

find_antonyms

Find antonyms for a word.

find_antonyms("happy")
-> {
    "antonyms": ["unhappy"]
}

batch_encode

Encode multiple words at once.

batch_encode(["happy", "sad", "neutral"])
-> {
    "results": [
        {"word": "happy", "valence": "positive"},
        {"word": "sad", "valence": "negative"},
        {"word": "neutral", "valence": "neutral"}
    ]
}

get_lexicon_info

Get information about the lexicon.

get_lexicon_info()
-> {
    "name": "Oyemi",
    "version": "3.2.0",
    "word_count": 145014
}

Code Format

Oyemi codes follow the format HHHH-LLLLL-P-A-V:

Component	Description	Values
HHHH	Semantic superclass	4-digit category code
LLLLL	Synset ID	5-digit unique identifier
P	Part of speech	1=noun, 2=verb, 3=adj, 4=adv
A	Abstractness	0=concrete, 1=mixed, 2=abstract
V	Valence	0=neutral, 1=positive, 2=negative

Use Cases

• AI Sentiment Analysis: Let AI agents understand emotional tone
• Semantic Grounding: Provide concrete valence scores instead of guessing
• Text Analysis: Analyze documents, reviews, feedback
• Word Relationships: Find synonyms, antonyms, similar words

License

MIT License

Author

Kaossara Osseni - grandnasser.com