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") |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the output (counts by status/phase/sponsor), but does not disclose whether it's a simple read operation, potential side effects, or error behavior (e.g., unknown condition). Adequate but not comprehensive.
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, front-loaded with action and output. Every sentence adds value with no wasted words.
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 the tool returns. However, it could mention output limits or format (e.g., JSON structure) to be fully 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 description coverage is 100%, so baseline is 3. The description repeats the grouping options already in the schema without adding new semantic meaning or examples beyond the generic one.
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 it gets clinical trial statistics and trends for a condition, listing specific output dimensions (status, phase, sponsors). Distinct from siblings get_trial_details and search_trials, which focus on individual trials or lists.
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?
Provides an example query to illustrate use ('How many Alzheimer's trials are recruiting right now?'), but does not explicitly contrast with siblings or state when not to use this tool. Implicit guidance is strong but could be more explicit.
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: searching for trials, getting detailed info on a specific trial, and obtaining statistics. No overlap in functionality.
All tool names follow a consistent verb_noun snake_case pattern: get_trial_details, get_trial_stats, search_trials.
Three tools is an appropriate scope for a clinical trials server, covering search, details, and statistics without being too few or too many.
The set covers the main operations: search, details, and statistics. However, some potential gaps exist, such as a tool to list all trials by sponsor or location, though search can approximate these.