PubMed Enhanced Search Server

Created By
leescota year ago
Overview

what is Pubmed Mcp Smithery?

Pubmed Mcp Smithery is a Model Content Protocol server designed to enhance the search and retrieval of academic papers from the PubMed database, offering additional features like MeSH term lookup and PICO-based evidence search.

how to use Pubmed Mcp Smithery?

To use the server, clone the repository from GitHub, install the necessary dependencies, and run the server locally. You can also integrate it with Claude Desktop by modifying the configuration file.

key features of Pubmed Mcp Smithery?

  • Keyword-based search with optional journal filtering
  • Sorting results by relevance or date
  • MeSH term lookup for related medical concepts
  • Publication count statistics for multiple search terms
  • Detailed paper information retrieval including abstracts and DOIs
  • Structured PICO-based searches with support for synonyms

use cases of Pubmed Mcp Smithery?

  1. Conducting literature reviews in medical research
  2. Comparing publication trends across different medical topics
  3. Finding relevant studies for evidence-based practice

FAQ from Pubmed Mcp Smithery?

  • What programming language is required to run the server?

Python 3.6 or higher is required.

  • Can I filter search results by journal?

Yes, the server supports optional journal filtering in search queries.

  • Is there a limit to the number of search terms I can use?

No, you can use multiple search terms for both publication counts and PICO searches.

Server Config

{
  "mcpServers": {
    "pubmed-mcp-smithery": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "@leescot/pubmed-mcp-smithery",
        "--config",
        "\"{}\""
      ]
    }
  }
}
Project Info
Created At
a year ago
Updated At
a year ago
Author Name
leescot
Star
-
Language
-
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
Crevio

2 days ago