PubTator MCP Server

Created By
JackKuo666a year ago
🔍 A biomedical literature annotation and relationship mining server based on PubTator3, providing convenient access through the MCP interface.
Overview

What is PubTator MCP Server?

PubTator MCP Server is a biomedical literature annotation and relationship mining server that provides convenient access to the PubTator3 system through the Model Context Protocol (MCP). It allows AI models to programmatically search scientific literature, obtain annotation information, and analyze entity relationships.

How to use PubTator MCP Server?

To use the PubTator MCP Server, you can either install it via Smithery or manually clone the repository and install dependencies. After installation, you can run the server directly or use Docker for deployment.

Key features of PubTator MCP Server?

  • Literature Annotation Export in multiple formats
  • Entity ID Lookup for biological concepts
  • Relationship Mining to discover biomedical relationships
  • Literature Search by keywords and entity IDs
  • Batch Processing for exporting annotation information

Use cases of PubTator MCP Server?

  1. Annotating biomedical literature for research purposes.
  2. Mining relationships between different biological entities.
  3. Facilitating AI models in accessing and analyzing scientific literature.

FAQ from PubTator MCP Server?

  • What programming language is used?

The server is built using Python.

  • Is there a rate limit for API requests?

Yes, the maximum request rate is 3 requests per second.

  • Can I use Docker for deployment?

Yes, a Dockerfile is provided for easy deployment.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
JackKuo666
Star
0
Language
Python
License
MIT license

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago