An implementation of the "Claimify" methodology for factual claim extraction, delivered as a local Model Context Protocol (MCP) server. This tool
ClaimsMCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. An implementation of the "Claimify" methodology for factual claim extraction, delivered as a local Model Context Protocol (MCP) server. This tool implements the multi-stage claim extraction approach detailed in the academic paper "Towards.
Claimify extracts verifiable, decontextualized factual claims from text using a sophisticated four-stage pipeline:
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
You will need one environment variable: OPENAI_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
gpt-4o (recommended) - gpt-4o-mini (faster and cheaper) - Python 3.10+: For proper type hints and Pydantic supportThis sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. It is maintained by AdamGustavsson; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against ClaimsMCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
{
"mcpServers": {
"claimify-local": {
"command": "/path/to/your/claimify-env/bin/python",
"args": [
"/path/to/your/project/claimify_server.py"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
gpt-4o (recommended) - gpt-4o-mini (faster and cheaper) - Python 3.10+: For proper type hints and Pydantic support| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.