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Glama

xpay Academic Research Collection

by sh.xpay.mcp.research

search_trials_by_condition

Search clinical trials by medical condition(s).

This tool allows you to search for clinical trials based on a list of medical conditions.

Input:

  • conditions: A list of strings, where each string is a medical condition to search for. The search will find trials related to any of the specified conditions. Example: ['cancer', 'diabetes']

  • max_studies: The maximum number of studies to return. Defaults to 50.

  • fields: A list of specific fields to return in the results. If not provided, returns SEARCH_TOOL_DEFAULTS (9 essential fields: NCTId, BriefTitle, Acronym, Condition, Phase, InterventionName, LeadSponsorName, OverallStatus, HasResults).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoSpecific fields to return
conditionsYesMedical conditions to search for
max_studiesNoMaximum number of studies to return

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral disclosure burden. It does add useful context: trials are matched by 'any of' the specified conditions, and it lists the default fields returned. However, it omits behaviors like pagination, error handling, or how queries are interpreted beyond the 'any of' statement, so it is not fully transparent.

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

Conciseness4/5

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

The description is well-structured with an opening purpose statement and a bulleted 'Input' section. It is slightly redundant in repeating that the tool searches by medical conditions, but every other sentence provides useful detail (defaults, examples, field list) without excessive verbosity.

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

Completeness4/5

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

Given the moderate complexity (3 parameters, no output schema), the description covers the essential context: what conditions match, how many results are returned, and which fields appear by default. It does not describe the raw response structure or error cases, but for a search tool with high schema coverage this is a reasonably complete description.

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

Parameters4/5

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

The input schema already provides 100% description coverage, but the description adds value by giving an example of the conditions parameter and explicitly enumerating the nine default fields returned when 'fields' is not provided. This goes beyond the schema by clarifying real-world usage and default output contents.

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 opens with a specific verb+resource: 'Search clinical trials by medical condition(s).' This clearly states what the tool does and, combined with the sibling tool names (search_trials_by_acronym, search_trials_by_intervention, etc.), distinguishes it as the condition-based search variant.

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

Usage Guidelines2/5

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

The description explains how to use the tool (provide conditions, max_studies, fields) but does not explicitly state when to prefer it over sibling trial search tools or when not to use it. The sibling names imply alternatives, but the description itself offers no comparative guidance.

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

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TDQS

C2.6/5.0
Disambiguation1/5

Multiple tools appear to serve the same purpose, such as search_arxiv and search-arxiv, or papers-search-basic, paper-search-advanced, search_papers, and search. The download/read tools for different sources follow similar patterns, but some return 'not supported' messages, making it unclear which tools are actually functional.

Naming Consistency1/5

Tool names mix snake_case, kebab-case, and bare verbs without a consistent pattern. For example, about_nanci, analysis-citation-network, download-full-paper-arxiv, fetch, and search_arxiv all coexist, and the same action for different sources alternates conventions (search-arxiv vs search_arxiv).

Tool Count1/5

With 53 tools, the server is heavily over-scoped. Many tools are redundant or near-duplicates, such as six source-specific search tools plus an aggregate search, and the inclusion of both paper and clinical trial tools in one server creates unnecessary bloat.

Completeness3/5

The server covers a wide range of research workflows, including search, download, read, citations, authors, and clinical trials. However, several tools (crossref/pubmed download/read) are non-functional dead ends, and the redundancy makes it harder to navigate the surface.

Resources