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?

The description adds behavioral context beyond the annotations (readOnlyHint, openWorldHint, idempotentHint) by stating the tool returns 'study counts per value'. This aligns with the annotations and provides operational insight without contradicting them.

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 with no wasted words. The first sentence immediately states the action and output, and the second provides concrete usage guidance and example fields, making it 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?

Given the tool's simplicity (one parameter with full schema description, output schema present, and rich annotations), the description fully covers the needed context: what it does, when to use it, and what output to expect (values with counts). No gaps remain.

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?

With 100% schema description coverage, the parameter is already well-documented. The tool description adds value by illustrating the purpose ('Discover valid values...with study counts per value') and giving usage examples, supplementing rather than repeating the schema.

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 uses the specific verb 'Discover' and clearly identifies the resource as 'valid values for ClinicalTrials.gov fields with study counts per value'. It provides concrete examples (OverallStatus, Phase, etc.) and distinguishes itself from the sibling tool 'clinicaltrials_get_field_definitions' by focusing on values rather than 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 explicitly states usage context: 'Use to explore available filter options before building a search'. It gives examples of fields to query, implying when to use it. However, it does not explicitly state when not to use it or contrast with alternatives beyond the implied distinction from field definitions.

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?

Annotations already declare readOnly, openWorld, and idempotent. The description adds useful behavioral context, notably the ~70KB record size and the recommendation to use the fields parameter to reduce payload. 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?

Three sentences: purpose, capabilities, and a concise tip. Every sentence adds value. No 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 complexity (18 parameters, no required, output schema present), the description provides a sufficient overview. It highlights the key payload consideration. The schema covers remaining 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% with detailed parameter descriptions. The description adds no significant meaning beyond the schema, just a high-level summary. Baseline applies.

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 the tool searches for clinical trial studies from ClinicalTrials.gov and lists key capabilities. However, it does not explicitly differentiate from sibling tools like clinicaltrials_find_eligible.

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 no guidance on when to use this tool versus alternatives or when not to use it. No exclusions or context for sibling tools are mentioned.

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