clinicaltrialsgov-mcp-server

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds critical context about re-ranking: 'Results are re-ranked so studies whose own condition matches a requested condition surface above tangential matches.' This explains an important behavioral nuance beyond annotations.

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?

The description is two sentences: the first states purpose and required inputs, the second explains re-ranking. It is front-loaded, concise, and contains no fluff. Every sentence adds value.

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?

Given the tool's complexity (7 parameters, nested objects, output schema exists), the description adequately covers purpose, required inputs, key behaviors (eligibility, re-ranking), and uses annotations for safety. It is complete for an AI agent to select and invoke correctly.

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?

Schema coverage is 100%, but the description adds meaningful extra context: for 'conditions', it explains OR logic and re-ranking; for 'location', it mentions that country is required and state/city narrow the match, and references an alternative for radius search. This goes beyond the schema alone.

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 clearly states the tool's purpose: 'Match patient demographics and conditions to eligible recruiting clinical trials.' It specifies the verb 'Match', the resource 'clinical trials', and the context of demographics and conditions, distinguishing it from siblings by focusing on eligibility and recruiting.

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 provides clear guidance on required inputs (age, sex, conditions, location) and explains the re-ranking behavior. However, it does not explicitly state when not to use this tool or compare it to alternatives like clinicaltrials_search_studies, leaving some ambiguity.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds behavioral context beyond annotations: it describes the nature of results (ranked matches for search, field list for drill, section summary for overview) and explains that it resolves field names from the data model, which is consistent with read-only, idempotent behavior.

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?

The description is front-loaded with the core purpose and then details modes. While slightly long, every sentence earns its place by providing essential usage information. It could be slightly more compact, but the structure is logical and clear.

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?

Given the tool's complexity (5 parameters, 1 required, output schema exists), the description is complete: it explains all modes, parameter dependencies (query required for search, path for drill), provides examples, and mentions integration with other tools. No gaps remain for an agent to effectively select and invoke the tool.

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?

Schema coverage is 100%, but the description adds meaningful context: for 'query' it provides examples ('enrollment', 'sponsor'), for 'path' it gives dot-notation examples, and for 'mode' it clarifies conditional requirements. This enriches parameter understanding beyond the schema alone.

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 clearly states it resolves valid field names from the ClinicalTrials.gov data model, listing example PascalCase identifiers. It distinguishes three modes (search, drill, overview) with specific behaviors, making the tool's purpose unambiguous and distinct from siblings.

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 explicitly explains when to use each mode: search for keyword matching, drill for section path exploration, overview for top-level summary. It also notes that the output is used as input to other tools like clinicaltrials_get_field_values, providing clear usage context.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. The description adds value by explaining that the tool returns 'valid values with study counts per value,' which is not in annotations. No contradictions.

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?

Two sentences, no redundancy. First sentence states core functionality; second sentence provides usage context and examples. Highly efficient.

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?

Despite only one parameter, the description fully covers what the agent needs: purpose, usage guidance, and parameter context. Output schema exists to handle return value details.

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?

Schema coverage is 100%. The description reinforces the parameter meaning by giving example values and explaining the purpose (getting valid values with counts). This adds context beyond the schema's technical description.

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?

Clearly states the tool discovers valid field values with study counts. Examples of fields and the verb 'Discover' specify the resource and action. Distinguishes from siblings like clinicaltrials_get_field_definitions (which provides field names) and clinicaltrials_search_studies (which searches studies).

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?

Explicitly says 'Use to explore available filter options before building a search,' providing clear context. No explicit when-not-to-use or alternatives, but the examples and context are sufficient for an AI agent to choose appropriately.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds that it is 'Fast and lightweight' and 'without fetching study data', providing useful behavioral context beyond the annotations.

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?

The description is two sentences, front-loaded with the core purpose and usage advice. Every sentence is informative and there is no repetition or fluff.

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?

Given the rich schema and annotations, the description covers the high-level use case and scenario. The output schema (mentioned) likely details return values, so the description is sufficiently complete for a well-documented tool.

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?

Schema coverage is 100% with detailed descriptions for all 11 parameters, so the description adds limited additional value. It mentions using filters to build breakdowns, which guides parameter use, but this is not extensive.

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 clearly states it 'Get total clinical trial study count' without fetching data, distinguishing it from sibling tools that retrieve records or results. It specifies the resource and action precisely.

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 advises using it for quick statistics or building breakdowns by calling multiple times with different filters, providing clear context. However, it does not explicitly contrast with alternatives like clinicaltrials_search_studies, leaving some ambiguity.

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

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

Annotations indicate read-only, open-world, and idempotent behavior. The description adds value by explaining that limit parameters trim results and that original totals are preserved in filtersApplied only when a cap actually trims. It also discloses the caveat for nearLocation that locations without coordinates are dropped and that distances are city-centroid granular.

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?

The description is concise with two main sentences plus parameter details. It is front-loaded with the primary purpose, then expands on optional behavior. Every sentence contributes value without redundancy.

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?

Given the tool's complexity (5 parameters, nested objects, output schema exists), the description covers all important aspects: main function, parameter behavior with caveats, and return structure (full record). The output schema handles return format details, so no further explanation needed.

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?

All parameters have schema descriptions (100% coverage). The description adds meaningful context beyond the schema: e.g., primary outcomes are never capped for outcomeLimit, nearLocation pairs naturally with locationLimit, and nearLocation explains coordinate and distance granularity. This fully compensates for any missing schema details.

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 clearly states the tool fetches a single clinical trial study by NCT ID from ClinicalTrials.gov, returning the full study record. It uses specific verb ('Fetch') and resource ('study by NCT ID'), and distinguishes itself from siblings like clinicaltrials_search_studies which searches multiple studies.

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 implicitly guides usage by specifying that it's for a single study. It also provides explicit context within the nearLocation parameter description, directing users to clinicaltrials_search_studies for broader geographic filtering. However, it lacks a general 'when to use' statement relative to all siblings.

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

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

Annotations already declare readOnly, openWorld, and idempotent hints. The description complements this by detailing the data content and the availability condition. It adds behavioral context without contradiction, though it omits some specifics like payload sizes for summary.

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?

Two sentences with front-loaded core purpose and immediate usage guidance. No unnecessary words; every sentence adds value.

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?

With an output schema present, the description covers purpose, usage condition, and prerequisite. It misses mentioning the summary parameter's benefit (payload reduction) which is only in the schema, but overall is fairly complete.

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?

Schema coverage is 100% with detailed parameter descriptions. The description does not add significant meaning beyond the schema, except for mentioning sections briefly. 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 clearly states that the tool fetches clinical trial results data from ClinicalTrials.gov for completed studies, listing specific data types (outcome measures, adverse events, etc.). It distinguishes from sibling tools by mentioning precondition (hasResults true) and suggesting use of clinicaltrials_search_studies first.

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 provides explicit guidance on when to use ('only available for studies where hasResults is true') and a prerequisite action ('Use clinicaltrials_search_studies first'). It does not explicitly state when not to use or list alternatives for other data, but the context is clear.

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

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

The description adds significant behavioral context beyond annotations: it explains the default return format (compact index), geoFilter behavior (re-sorts locations and annotates with distance), the EnrollmentCount sentinel and includeUnknownEnrollment parameter, and that full records are ~70KB. This goes well beyond the readOnlyHint, openWorldHint, and idempotentHint annotations.

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?

The description is concise yet comprehensive, front-loading the primary purpose. It covers all key aspects without unnecessary verbosity, using natural language that is easy to parse. Every sentence adds value, and the structure flows logically from general purpose to specific parameter details.

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?

Given the high parameter count (18), full schema coverage, and presence of an output schema, the description is remarkably complete. It explains behavioral nuances for complex parameters (geoFilter, sort, includeUnknownEnrollment), references sibling tools for additional support, and provides enough context for an AI agent to use the tool correctly without gaps.

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?

Despite 100% schema coverage, the description adds substantial value to parameter semantics: it provides examples for sort, explains reserved characters in query, recommends using clinicaltrials_get_field_definitions for fields, details geoFilter's re-sorting behavior, gives advancedFilter examples, and clarifies the sentinel for includeUnknownEnrollment. This exceeds the baseline of 3.

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 clearly states the tool's purpose: 'Search for clinical trial studies from ClinicalTrials.gov.' It lists specific capabilities (full-text/field-specific queries, status/phase/geographic filters, pagination, sorting, field selection) and distinguishes from sibling tools by noting it returns a compact per-study index by default, contrasting with full study records (~70KB) and referencing clinicaltrials_get_field_definitions for field definitions.

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 provides guidance on when to use specific parameters (e.g., 'For field-scoped searches, use the dedicated *Query parameters... or advancedFilter') and gives advice for common queries (e.g., 'For largest trials queries, pair EnrollmentCount:desc with advancedFilter...'). It implies when not to use (e.g., for full records, use clinicaltrials_get_study_record) but does not explicitly state alternatives for every scenario.

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