How do I install the Osseni94/oyemi-mcp MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "oyemi-mcp": { "command": "npx", "args": ["-y", "Osseni94/oyemi-mcp"] } }

Osseni94/oyemi-mcp

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🐍 🏠 - Deterministic semantic word encoding and valence/sentiment analysis using 145K+ word lexicon. Provides word-to-code mapping, semantic similarity, synonym/antonym lookup with zero runtime NLP dependencies.

Quick Install

One-Click IDE Configuration

claude_desktop_config.json

{
  "mcpServers": {
    "osseni94-oyemi-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "osseni94-oyemi-mcp"
      ]
    }
  }
}

Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.

Documentation Overview

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