Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

trials_search

Read-onlyIdempotent

Search ClinicalTrials.gov for clinical studies by condition, intervention/drug, sponsor, recruitment status, and/or location. Returns each trial's NCT id, title, status, conditions, lead sponsor, phase, and study type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termNoGeneral search term.
limitNoMax rows (default 10, max 50).
statusNoRecruitment status, e.g. 'RECRUITING', 'COMPLETED', 'TERMINATED'.
sponsorNoSponsor/organization, e.g. 'Pfizer'.
locationNoLocation, e.g. 'Houston' or 'Texas'.
conditionNoDisease/condition, e.g. 'breast cancer'.
interventionNoDrug/intervention, e.g. 'semaglutide'.

TDQS

A4/5.0
Behavior3/5

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

Annotations already establish that this is a safe, read-only, idempotent operation. The description adds that it queries an external registry and returns summary trial fields, which is useful, but it does not disclose behaviors like pagination, result limits, or data-source caveats. No contradiction with annotations.

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?

Two concise sentences: the first states purpose and search facets, the second states the returned fields. There is no filler, repetition, or unnecessary detail.

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?

For a read-only search tool with a fully described parameter schema and no output schema, the description covers the purpose, search criteria, and return fields. It could optionally mention the relationship to trials_details, but an agent has enough information to invoke it correctly.

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

Parameters3/5

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

The input schema covers all 7 parameters with descriptions (100% coverage), so the description's list of facets adds only marginal meaning beyond the schema. It accurately names the key search dimensions but does not explain semantics like 'term' vs 'condition' or the default/maximum for 'limit'.

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 states a specific verb ('Search'), a specific resource ('ClinicalTrials.gov'), the subject ('clinical studies'), and the main search dimensions (condition, intervention/drug, sponsor, recruitment status, location). It also lists the return fields, which distinguishes it from the sibling trials_details tool.

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 explicitly identifies the scenarios in which this tool is appropriate: searching for studies by condition, sponsor, status, etc. It does not name alternatives or exclusions, such as when to prefer trials_details, but the intended use is clear enough for an agent to select it.

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

All LiveDataLink toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources