list_clinical_trialsList clinical trials from public registries (conditions, status, intervention type). Filter by condition, status (e.g. RECRUITING), or keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Results per page (1–100, default 20). | |
| offset | No | Number of results to skip (default 0). | |
| search | No | Keyword search across title and description. | |
| status | No | Filter by trial status, e.g. RECRUITING. | |
| condition | No | Filter by condition. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden for behavioral disclosure. It only mentions 'public registries' but omits critical details like pagination behavior, rate limits, required permissions, or result format. The agent is left uninformed about side effects or limitations.
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 very short (one sentence) but omits important details, making it under-specified rather than concise. The inclusion of a possibly inaccurate parenthetical reduces clarity. It could be more efficient with better structure.
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 5 optional parameters, no output schema, and no annotations, the description is incomplete. It fails to explain the return format, pagination (though limit/offset exist in schema), or how filtering interactions work. The agent lacks essential context for correct invocation.
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 each parameter having a description. The description adds some context by naming filters but also mentions an unsupported 'intervention type' filter, which could mislead. Overall, the description adds minimal value beyond what the schema already provides.
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 lists clinical trials from public registries and mentions filtering by condition, status, or keyword. This distinguishes it from siblings like get_clinical_trial. However, it also mentions 'intervention type' which is not a parameter in the schema, causing slight confusion.
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 on when to use this tool versus alternatives like get_clinical_trial or list_blog_posts. The description does not provide any when-to-use or when-not-to-use context, leaving the agent to infer purpose from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: separate get/list for different data types (blog posts, clinical trials, research papers), distinct prediction tools (clintox, dti, ppi), and separate search tools (compounds vs. broad search). No two tools appear to overlap.
Most tools follow the verb_noun pattern (e.g., get_blog_post, list_clinical_trials, predict_dti). The only outlier is mammal_health, which uses a different structure (noun_noun), causing minor inconsistency.
With 15 tools, the server covers a broad oncology research domain without being overwhelming. Each tool serves a clear role, and the count feels well-scoped for the stated purpose.
The tool set covers retrieval and prediction for key domains (papers, trials, drugs, compounds) and includes a cross-dataset search. Minor gaps exist, such as the lack of a dedicated get_compound tool, but search_oncology can partially compensate.