Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
An MCP server for natural language inference
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Pick the AI app you use, then copy the settings.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"omni-nli": {
"command": "uvx",
"args": [
"omni-nli"
]
}
}
}Run this in your terminal
claude mcp add omni-nli -- uvx omni-nliPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"omni-nli": {
"command": "uvx",
"args": [
"omni-nli"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"omni-nli": {
"type": "stdio",
"command": "uvx",
"args": [
"omni-nli"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026
The registry name was verified with the GitHub account CogitatorTech, which owns the repo
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 9 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, CLIO Paraview
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
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Omni-NLI
A multi-interface (REST and MCP) server for natural language inference
Omni-NLI is a self-hostable server that provides natural language inference (NLI) capabilities via RESTful and the Model Context Protocol (MCP) interfaces. It can be used both as a very scalable standalone stateless microservice (via the REST API) and also as an MCP server for AI agents to implement a verification layer for AI-based applications.
Given two pieces of text called premise and hypothesis, NLI (AKA textual entailment) is the task of determining the directional relationship between them as it is perceived by a human reader. The relationship is given one of these three labels:
"entailment": the hypothesis is supported by the premise"contradiction": the hypothesis is contradicted by the premise"neutral": the hypothesis is neither supported nor contradicted by the premise[!IMPORTANT]
NLI is not the same as logical entailment.
Its goal is to determine if a reasonable human would consider the hypothesis to follow from the premise.
This checks for consistency instead of the absolute truth of the hypothesis.
Typical applications of NLI include:
[!IMPORTANT]
The quality of the results depends a lot on the model (the LLM) that is used.
A good strategy is to first fine-tune the model using a dataset of premise-hypothesis-label triples that are relevant to your application domain.
See ROADMAP.md for the list of implemented and planned features.
[!IMPORTANT]
Omni-NLI is in early development, so bugs and breaking changes are expected.
Please use the issues page to report bugs or request features.
pip install omni-nli[huggingface]
omni-nli
curl -X POST \
-H "Content-Type: application/json" \ -d '{ "premise": "A football player kicks a ball into the goal.", "hypothesis": "The football player is asleep on the field." }' \ http://127.0.0.1:8000/api/v1/nli/evaluate
Example response:
{
"label": "contradiction", "confidence": 0.99, "model": "microsoft/Phi-3.5-mini-instruct", "backend": "huggingface" }
Check out the Omni-NLI Documentation for more information, including configuration options, API reference, and examples.
Contributions are always welcome! Please see CONTRIBUTING.md for details on how to get started.
Omni-NLI is licensed under the MIT License (see LICENSE).