# Osseni94/oyemi-mcp [Health: Active]

**Category:** 📊 Data Platforms  
**Repository:** https://github.com/Osseni94/oyemi-mcp  
**GitHub Stars:** 3  
**Views:** 3  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/osseni94-oyemi-mcp

## Description
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.

## Tools
Capabilities this server exposes over MCP:

- **encode_word** — Encode a word to its semantic code.
- **analyze_text** — Analyze the valence/sentiment of text.
- **semantic_similarity** — Compare two words semantically.
- **find_synonyms** — Find synonyms for a word.
- **find_antonyms** — Find antonyms for a word.
- **batch_encode** — Encode multiple words at once.
- **get_lexicon_info** — Get information about the lexicon.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):

```json
"mcpServers": {
  "oyemi-mcp": {
    "command": "uvx",
    "args": ["oyemi-mcp"]
  }
}
```

## Documentation

## What Osseni94/oyemi-mcp MCP server does

Osseni94/oyemi-mcp MCP server gives MCP-compatible agents access to the Oyemi semantic lexicon. Its tools encode words into structured semantic codes, inspect emotional valence, compare word meaning, and retrieve related terms. The lexicon contains 145,014 words according to the provided version information.

The server is intended for deterministic, lexicon-based analysis rather than runtime machine-learning inference. It can help an agent classify the tone of text, ground word-level reasoning in fixed metadata, or inspect relationships between terms.

## How it works

The server maps a word to an Oyemi code with the format `HHHH-LLLLL-P-A-V`. The segments represent a semantic superclass, synset identifier, part of speech, abstractness, and valence. Part-of-speech values distinguish nouns, verbs, adjectives, and adverbs; valence values identify neutral, positive, or negative terms.

`analyze_text` calculates text valence and reports a sentiment label along with detected positive and negative words. For example, text containing both hopeful and anxious language can produce a neutral overall score while still identifying those terms individually. Similarity results include a numeric score and a relationship description, while synonym and antonym lookups return related word lists.

Osseni94/oyemi-mcp MCP server has no external NLP libraries listed as runtime requirements. Its behavior is based on the bundled semantic lexicon and its defined word relationships, making it suitable when repeatable lexical results are preferred.

## Setup and configuration

Install the Python package with:

```bash
pip install oyemi-mcp
```

The repository also documents installation from source by cloning the project and running `pip install -e .` from its directory. After installation, the executable is named `oyemi-mcp`.

For Claude Desktop, add an `oyemi` entry to `claude_desktop_config.json` with `oyemi-mcp` as the command. The README provides the same command configuration pattern for Claude Code. No API key, token, or environment variable is specified in the provided material.

## Tools and capabilities

Osseni94/oyemi-mcp MCP server exposes these tools:

- `encode_word`: Return a word’s semantic code and metadata such as part of speech, abstractness, and valence.
- `analyze_text`: Calculate valence and identify positive or negative words in text.
- `semantic_similarity`: Compare two words and return similarity information.
- `find_synonyms`: Retrieve synonyms for a word.
- `find_antonyms`: Retrieve antonyms for a word.
- `batch_encode`: Encode multiple words in one request.
- `get_lexicon_info`: Report lexicon metadata, including its name, version, and word count.

## Limitations and notes

The provided material describes a lexicon-based system, not a general-purpose NLP pipeline. Results depend on the words and relationships available in the Oyemi lexicon. The examples show word-level lookups and text valence analysis, but do not document language coverage beyond the supplied lexicon, custom lexicon loading, configuration options, or additional analysis methods.

The README lists MIT licensing and identifies the lexicon version in its example output as 3.2.0. It does not describe authentication, hosted deployment, or paid service requirements.

_Full upstream README: https://allmcps.com/mcp/osseni94-oyemi-mcp/readme_