AgentClinical — Clinical trials intelligence
Server Details
ClinicalTrials.gov intelligence — search 400K+ trials by condition, intervention, sponsor, or phase. Get full trial details, eligibility criteria, and aggregate stats.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.7/5 across 3 of 3 tools scored. Lowest: 3.1/5.
Each tool serves a distinct purpose: searching trials, getting full details of a specific trial, and retrieving aggregate statistics. There is no overlap or ambiguity.
All tools follow a consistent verb_noun pattern: search_trials, get_trial_details, get_trial_stats. Naming is predictable and clear.
Three tools is slightly low but reasonable for a focused read-only API covering search, detail retrieval, and statistics. The count does not feel excessive or inadequate.
The set covers the main use cases: finding trials, inspecting their details, and getting overview statistics. Missing potentially useful features like listing conditions or sponsors, but core functions are present.
Available Tools
3 toolsget_trial_detailsAInspect
Get full details of a specific clinical trial by NCT ID. Returns title, summary, description, eligibility criteria, primary/secondary outcomes, sponsor, collaborators, locations, and results summary if available.
| Name | Required | Description | Default |
|---|---|---|---|
| nct_id | Yes | ClinicalTrials.gov NCT ID (e.g. "NCT03232697") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden for behavioral traits. It only describes what data is returned, not operational aspects like read-only nature, authentication, rate limits, or error handling. Agent may need more context.
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?
Single sentence, no wasted words, front-loaded with purpose. 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?
For a simple retrieval tool with one parameter and no output schema, description lists all returned fields. Could mention error handling or data freshness, but overall adequate.
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%, and the description adds no new meaning beyond 'by NCT ID', which is already in the schema. Baseline score 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?
Description explicitly states 'get full details of a specific clinical trial by NCT ID' and lists the returned fields, making the purpose clear. It distinguishes from siblings like search_trials (list) and get_trial_stats (statistics).
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?
Implies usage when an NCT ID is available. Does not explicitly state when not to use or provide alternatives, but the context of sibling tool names gives some guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_trial_statsAInspect
Get clinical trial statistics and trends for a condition. Returns counts by status (recruiting, completed, etc.), by phase, and top sponsors. Useful for: "How many Alzheimer's trials are recruiting right now?"
| Name | Required | Description | Default |
|---|---|---|---|
| group_by | No | Grouping dimension: status (default), phase, or sponsor | status |
| condition | Yes | Disease or condition to analyze (e.g. "alzheimer", "lung cancer", "covid") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the tool returns counts grouped by status/phase/sponsor, implying a read-only, non-destructive operation. Behavior is clearly statistical aggregation.
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 plus an example — concise, front-loaded, 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 no output schema, the description adequately explains what is returned (counts by status, phase, sponsors). Both parameters are covered. No gaps for this type of 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 good parameter descriptions. The description adds minimal extra value (example conditions, grouping hint). Baseline of 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 the tool's purpose: 'Get clinical trial statistics and trends for a condition.' It specifies what it returns (counts by status, phase, and top sponsors) and distinguishes from sibling tools by focusing on aggregate data rather than individual trial details.
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 a clear use case example ('How many Alzheimer's trials are recruiting right now?') and implies the tool is for aggregate statistics. Lacks explicit comparison to siblings but is sufficient given context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_trialsBInspect
Search clinical trials on ClinicalTrials.gov by condition, intervention, sponsor, status, or phase. Returns trial IDs, titles, status, phases, conditions, interventions, enrollment, and locations count.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (max 20) | |
| phase | No | Trial phase: PHASE1, PHASE2, PHASE3, PHASE4 | |
| status | No | Trial status: RECRUITING, COMPLETED, ACTIVE_NOT_RECRUITING, NOT_YET_RECRUITING, TERMINATED | |
| sponsor | No | Sponsor name (e.g. "Pfizer", "NIH", "Mayo Clinic") | |
| condition | No | Disease or condition (e.g. "diabetes", "breast cancer", "alzheimer") | |
| intervention | No | Drug, device, or intervention (e.g. "metformin", "pembrolizumab") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are available, so the description carries full burden. It does not disclose behavioral traits such as search logic (exact match vs. fuzzy), pagination, rate limits, data freshness, or authentication needs. The agent has limited insight into how the search works beyond the listed parameters.
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 highly concise: two sentences covering purpose and return information. It is front-loaded and contains no superfluous details, making it efficient for agent parsing.
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 moderate complexity (6 parameters, no required), the description covers purpose and return fields adequately. However, it lacks usage guidance and behavioral details, making it incomplete for an agent to fully understand the tool's behavior without external knowledge.
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), so the description adds minimal value beyond listing the criteria. It does not explain how multiple parameters interact (e.g., AND/OR logic) or the maximum limit (though schema mentions max 20). 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 it searches clinical trials on ClinicalTrials.gov and lists specific search criteria. It mentions return fields, giving a clear purpose. However, it does not explicitly differentiate from sibling tools like get_trial_details or get_trial_stats, leaving some ambiguity.
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?
No guidance is provided on when to use this tool vs. alternatives. The description lacks when-not-to-use scenarios or references to sibling tools, leaving the agent to infer context without explicit direction.
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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