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Glama

xpay Academic Research Collection

get_trial_details

Get comprehensive details for a single clinical trial.

This tool retrieves detailed information for a single clinical trial given its NCT ID.

Input:

  • nct_id: The NCT ID of the trial to retrieve. Example: 'NCT04280705'

  • fields: A list of specific fields to return. If not provided, returns DETAIL_TOOL_DEFAULTS (25 comprehensive fields covering: NCTId, BriefTitle, OfficialTitle, Acronym, Condition, Keyword, Phase, OverallStatus, InterventionType, InterventionName, InterventionDescription, ArmGroupLabel, ArmGroupType, ArmGroupDescription, EligibilityCriteria, MinimumAge, MaximumAge, Sex, PrimaryOutcomeMeasure, SecondaryOutcomeMeasure, BriefSummary, LocationFacility, LocationCountry, LeadSponsorName, CollaboratorName, HasResults).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoSpecific fields to return
nct_idYesNCT ID of the trial to retrieve

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses the default behavior: 'If not provided, returns DETAIL_TOOL_DEFAULTS' and lists the 25 fields covered. This goes beyond the schema by explaining what data the agent will receive, though it doesn't disclose the full response structure or error handling.

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: a clear opening sentence, then a concise explanation of inputs with an example and a detailed default field list. Every sentence serves a purpose, and the format is easy to scan.

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 tool has 2 parameters and no output schema, the description does a good job explaining the return default fields. It could be more complete by describing the response format or mentioning error cases, but the list of 25 fields gives the agent a solid understanding of what to expect.

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 schema already describes both parameters (100% coverage), so the baseline is 3. The description adds value with an example NCT ID ('NCT04280705') and elaborates on the 'fields' parameter by explaining the default field set. This extra context helps the agent understand how to use the parameters.

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's purpose: 'Get comprehensive details for a single clinical trial' and 'retrieves detailed information for a single clinical trial given its NCT ID.' The verb 'retrieves' and resource 'single clinical trial' are specific, and the use of 'single' distinguishes it from the sibling tool get_trial_details_batched.

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 context is clear: use this tool when you have an NCT ID and need detailed trial information. The description says 'given its NCT ID' but does not explicitly discuss when to use alternatives like get_trial_details_batched or mention exclusions. This is clear context without explicit alternatives, warranting a 4.

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