Skip to main content
Glama

search_europepmc_papers

Search Europe PMC for scientific papers using keywords; retrieve metadata like titles and authors. Optionally set max results and result type (lite or core).

Instructions

Search Europe PMC for papers without saving results to a file.

This function wraps the _search_europepmc_papers function and disables file saving. It retrieves metadata about papers matching the given keywords.

Args: keywords (str): The search query string containing keywords to look for. max_results (int, optional): The maximum number of results to return. Defaults to 10. result_type (str, optional): The type of results to retrieve. Options include "lite" (basic metadata) and "core" (detailed metadata). Defaults to "lite".

Returns: list[dict]: A list of dictionaries, where each dictionary contains metadata about a paper matching the search query.

Example: >>> results = search_europepmc_papers("machine learning", max_results=5) >>> for paper in results: ... print(paper["title"])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYes
max_resultsNo
result_typeNolite
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description lacks behavioral details such as mutability, authentication, rate limits, or side effects. It only mentions that file saving is disabled.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose, args/returns sections, and an example. It is front-loaded and concise without unnecessary text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all three parameters, explains return type and structure, and includes an example. Despite no output schema, it provides sufficient context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides full parameter definitions including types, defaults, and options for result_type, which is absent from the input schema. Schema coverage is 0%, so description compensates entirely.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches Europe PMC for papers, with the specific caveat of not saving results to a file. This distinguishes it from siblings that focus on identifiers, full text, or PDFs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates it is for searching without file saving, but does not explicitly state when to use alternatives. However, sibling tools have distinct purposes, so context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/contextualizer-ai/artl-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server