MCP PubMed Search
The MCP PubMed Search server enables you to:
Search PubMed: Query the medical literature database with customizable parameters
Control Results: Limit search results (1-15, default: 15)
Manage Notes: Store and access notes with a custom
note://URI schemeAdd Notes: Create new notes with specified names and content
Summarize Notes: Generate brief or detailed summaries of all stored notes
Allows searching PubMed, a database of biomedical literature, via MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP PubMed Searchsearch for recent studies on Alzheimer's disease treatments"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
pubmed_search MCP server
search pubmed via MCP
Components
Resources
The server implements a simple note storage system with:
Custom note:// URI scheme for accessing individual notes
Each note resource has a name, description and text/plain mimetype
Prompts
The server provides a single prompt:
summarize-notes: Creates summaries of all stored notes
Optional "style" argument to control detail level (brief/detailed)
Generates prompt combining all current notes with style preference
Tools
The server implements one tool:
add-note: Adds a new note to the server
Takes "name" and "content" as required string arguments
Updates server state and notifies clients of resource changes
Related MCP server: pubmed-search
Configuration
[TODO: Add configuration details specific to your implementation]
Quickstart
Install
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development
Building and Publishing
To prepare the package for distribution:
Sync dependencies and update lockfile:
uv syncBuild package distributions:
uv buildThis will create source and wheel distributions in the dist/ directory.
Publish to PyPI:
uv publishNote: You'll need to set PyPI credentials via environment variables or command flags:
Token:
--tokenorUV_PUBLISH_TOKENOr username/password:
--username/UV_PUBLISH_USERNAMEand--password/UV_PUBLISH_PASSWORD
Debugging
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm with this command:
npx @modelcontextprotocol/inspector uv --directory /Users/nofuture/Documents/GitHub/pubmed_search run pubmed-searchUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
Available Tools
1 toolpubmed_searchC
Search PubMed medical literature database
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Medical/scientific search query | |
| max_results | No | Maximum number of results (1-15) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions searching but doesn't describe response format, pagination, rate limits, authentication needs, or error conditions. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized and front-loaded, communicating the core purpose immediately without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the search returns, how results are structured, or any behavioral constraints. The 100% schema coverage helps with parameters but doesn't compensate for the lack of output and behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, thoroughly documenting both parameters (query and max_results). The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and target resource ('PubMed medical literature database'), providing a specific verb+resource combination. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It simply states what the tool does without offering usage instructions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined as searching the PubMed database, leaving no room for misselection.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The tool name 'pubmed_search' follows a clear verb_noun pattern that is appropriate for its function.
One tool is too few for a server named 'MCP PubMed Search', which suggests a broader scope for interacting with PubMed. A single search tool feels thin and incomplete, lacking operations like filtering results, retrieving article details, or managing queries.
The tool surface is severely incomplete for a PubMed search domain. While searching is a core function, there are obvious gaps such as no ability to get article metadata, filter searches, save results, or handle citations, which limits agent workflows significantly.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Auditable MCP server for PubMed, Europe PMC, ClinicalTrials.gov, and bioRxiv/medRxiv queries
PubMed MCP — wraps the NCBI E-utilities API (biomedical literature, free, no auth)
Search biomedical literature, get article details, find related articles, and explore MeSH terms
Related MCP Servers
- AlicenseAqualityCmaintenanceAn MCP server that provides access to PubMed articles through the Entrez API.2171MIT
- AlicenseNot gradedqualityFmaintenanceMCP server for PubMed search and literature summarization52MIT
- AlicenseBqualityDmaintenanceThis MCP server provides 16 intelligent tools for searching, retrieving, and linking biomedical literature from PubMed and PMC. It enables LLM applications to perform complex queries, batch processing, and cross-database linking.168MIT
- AlicenseNot gradedqualityCmaintenanceThis server enables searching PubMed articles, fetching metadata, and downloading PMC PDFs.4MIT
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