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clinicaltrialsgov-mcp-server

by io.github.cyanheads

Clinicaltrials Get Field Values

clinicaltrials_get_field_values
Read-onlyIdempotent

Discover valid values for ClinicalTrials.gov fields with study counts per value. Use to explore available filter options before building a search — e.g., valid OverallStatus, Phase, InterventionType, StudyType, or LeadSponsorClass values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsYesPascalCase field name(s) to get value statistics for — an empty list is rejected, not treated as "every field". Examples: OverallStatus, Phase, StudyType, Sex, LeadSponsorClass. Use clinicaltrials_get_field_definitions with a query to find more field names.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
fieldStatsNoOne entry per requested field: canonical path, PascalCase piece name, data type, missing/unique counts, and top values with study counts (or trueCount/falseCount for BOOLEAN fields).

TDQS

A5/5.0
Behavior5/5

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

The annotations (readOnlyHint, openWorldHint, idempotentHint) already indicate a non-destructive operation. The description adds context about returning study counts per value without contradicting any annotation. No annotation contradiction.

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 two sentences, no redundancy, and front-loads the core action ('Discover valid values') before the usage hint. Every word contributes to understanding.

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

Completeness5/5

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

An output schema exists, so return values are already specified. The description adds the key detail of 'study counts per value' and the usage context, making it complete for an agent to decide when and how to call it.

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

Parameters5/5

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

The schema has 100% description coverage for the 'fields' parameter. The description reinforces this with examples (OverallStatus, Phase, etc.) and clarifies edge cases like the rejection of an empty list, which goes beyond the schema.

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: 'Discover valid values for ClinicalTrials.gov fields with study counts per value.' It also provides concrete examples (OverallStatus, Phase, etc.) and distinguishes it from sibling tools like clinicaltrials_search_studies by focusing on filter options.

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

Usage Guidelines5/5

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

The description explicitly says to 'Use to explore available filter options before building a search,' which gives a clear when-to-use directive. It implicitly differentiates from search and count tools by positioning it as a precursor to search.

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

A4.7/5.0
Disambiguation5/5

Each tool targets a distinct action: finding eligible trials, resolving field names, discovering field values, counting studies, fetching full records, fetching results, and searching. Even the count tool is clearly separated from search by its lightweight, no-data purpose. No two tools have ambiguous boundaries.

Naming Consistency5/5

All tools follow a strict 'clinicaltrials_' prefix followed by a descriptive verb_noun pattern (find_eligible, get_study_record, search_studies). The naming is uniform in snake_case and clearly indicates the operation and object. This consistency makes the tool set predictable.

Tool Count5/5

Seven tools is well within the ideal range for a focused MCP server. Each tool covers a necessary aspect of the ClinicalTrials.gov API (search, retrieval, counting, field exploration, results, eligibility matching) without bloat or redundancy. The scope feels complete for a read-only clinical trials interface.

Completeness5/5

The tool set covers the full read-only lifecycle: discovering the data model, searching, counting, fetching protocol records, fetching results, and matching patients to trials. There are no obvious gaps like missing location details or result statistics (both handled via parameters). The server fulfills its stated purpose without dead ends.

Resources