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'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish safe read-only, non-destructive behavior, so there is no contradiction. The description adds disclosure of the response contents by enumerating the returned fields, which is concrete and useful. It doesn't cover failure behavior such as an invalid NCT id, but that is a minor gap for a simple lookup.
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 sentence, front-loaded with the verb and resource, followed by a compact field list. There is no filler or repetition of schema metadata.
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-key, read-only, idempotent lookup with rich annotations and a fully described parameter, the definition tells the agent exactly what will be returned. Without an output schema, enumerating the fields is necessary and sufficient; only error-handling details are absent.
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 schema fully describes nct_id, including the same example, so schema coverage is 100%. The description reinforces that the NCT id is the lookup key but adds no format or constraints beyond the schema. Baseline 3 is appropriate.
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 opens with a specific verb ('Get'), a precise resource ('full detail for one clinical trial'), and the required key ('NCT id'). Listing the returned fields differentiates it clearly from sibling search tools like trials_search, which would be used to discover trials without an id.
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 phrase 'by its NCT id' makes the trigger condition explicit: the agent should call this when it already has an exact trial identifier and needs full detail. It does not explicitly name alternatives or state when not to use it, but the context is clear enough.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.