About Osseni94/oyemi-mcp
Osseni94/oyemi-mcp is an MCP server published by Osseni94 in the Data category: mCP support for Oyemi Library.
The repository has 3 stars and 2 forks, with the last commit 10 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/Osseni94/oyemi-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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- ✓README presentThe repository ships documentation.
- !License declaredNo license file. Legally you have no right to use it in a product.
- !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
- !Dependencies pinnedNo lockfile. Installs can pull different code over time.
- ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
README
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
README fetched from the repository 23 hours ago. The original is authoritative.