oyemi-mcp

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.

- 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

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

Add to yourclaude_desktop_config.json:

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

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

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

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

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

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

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

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

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

Oyemi codes follow the formatHHHH-LLLLL-P-A-V:

- 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

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