Get full detail for one clinical trial by its NCT id (e.g. 'NCT02562313'): title, status, conditions, sponsor, phase, interventions, brief summary, enrollment, start/completion dates, number of sites, and the study URL.
| Name | Required | Description | Default |
|---|---|---|---|
| nct_id | Yes | ClinicalTrials.gov NCT id, e.g. 'NCT02562313'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as a read-only, idempotent, non-destructive operation, so the safety profile is covered. The description adds value by specifying exactly what the response contains, including study status, sponsor, phase, enrollment, dates, site count, and URL. No behavioral contradictions exist.
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?
One dense, front-loaded sentence conveys the operation, the identifier format, and a full list of returned fields. There is no filler, redundancy, or unnecessary qualification.
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 single-parameter, read-only lookup tool, the description is complete: it states how to call it, what input is expected, and what output fields to expect. No output schema exists, so the enumerated field list effectively fills that gap. Error handling or rate limits are not essential for this simple, safe operation.
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 already documents the single required parameter nct_id and provides the same example ('NCT02562313') found in the description. The description adds no new parameter semantics beyond what the schema supplies, so the baseline of 3 applies per the rubric.
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 states a specific verb ('Get full detail') and resource ('one clinical trial by its NCT id'), with a concrete example. It enumerates the returned fields, making it unmistakable what this tool does. It is clearly differentiated from its sibling trials_search, which is for finding trials rather than retrieving details for one known trial.
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 makes the usage context clear: use this tool when you have a specific NCT id and need comprehensive details for one clinical trial. It does not explicitly mention that trials_search should be used when only keywords or filters are available, nor does it give explicit exclusions, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.