PubMed MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PUBMED_EMAIL | Yes | Your email address (required by NCBI) | |
| PUBMED_API_KEY | No | Optional API key for higher rate limits |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_pubmedC | Search PubMed for scientific articles. |
| fetch_summaryC | Fetch detailed article information from PubMed using PMIDs. |
| get_fulltextC | Get full text content of PubMed articles using PMIDs. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose with no overlap: fetch_summary retrieves article metadata, get_fulltext provides full content, and search_pubmed finds articles. The descriptions make it easy for an agent to choose the right tool for each task without confusion.
All tool names follow a consistent verb_noun pattern (fetch_summary, get_fulltext, search_pubmed) with clear, action-oriented verbs. The naming is uniform and predictable across the set, making it easy to understand each tool's function.
With only 3 tools, the count feels thin for a PubMed domain that typically involves more operations like filtering, citation management, or advanced search. While the tools cover basic needs, the scope could benefit from additional functionality to be fully robust.
The tools cover core functions (search, summary, fulltext) but lack operations for updating, deleting, or managing articles, as well as advanced features like filtering by date or author. This creates minor gaps that agents might need to work around for complex workflows.