Cancel a queued or running STORM or Dataset agent job.
Authenticated, owner-scoped, idempotent.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond the annotations: it specifies authentication and owner-scoping, which are not in the annotations. It also reinforces the destructive and idempotent nature, though idempotency is already in annotations. It does not mention what happens to the job after cancellation or the response format, but the output schema covers that.
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 concise sentences, front-loaded with the primary action and followed by key behavioral traits. No wasted 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?
For a simple single-parameter tool with annotations and an output schema, the description is largely complete. It states the purpose, the target state (queued/running), and access constraints. It could mention behavior for already-completed jobs or cancellation confirmation, but the output schema likely covers the response.
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?
The schema includes a single required parameter, job_id, with no description, and the description does not elaborate on it beyond the context of canceling an agent job. Since the parameter is self-explanatory and the tool description gives some context, the meaning is clear, but the description does not compensate fully for the 0% schema coverage.
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 action: cancel a queued or running STORM or Dataset agent job. It uses a specific verb (cancel) and resource (agent job) with qualifiers (queued/running, STORM/Dataset) that distinguish it from sibling tools like agent_job_status or build_dataset. The purpose is immediately understandable.
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 context: use this when you need to cancel a queued or running agent job, and it notes the owner-scoped and authenticated nature, implying access restrictions. It does not explicitly list alternatives or when not to use it, but the 'queued or running' qualifier implies it is not for completed jobs.
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.
There is notable overlap among search, search_web, search_restaurants, and search_salons, as well as between filter_restaurants/filter_salons and search with constraints. However, descriptions clarify the intended vertical or corpus, and entity getters are distinct. The overlap is manageable but could cause misselection.
Names mostly follow a get_/list_/search_/register_/delete_/submit_/vote_ pattern in snake_case. Minor deviations like 'recall', 'remember', 'research', and 'travel_health' are less predictable but still readable. Overall consistent and clear.
38 tools is on the heavy side for a single MCP server, exceeding the typical well-scoped range. While the server covers multiple subdomains (search, travel disruptions, memory, feedback, research), the sheer number may overwhelm agents and suggests potential consolidation.
The tool surface covers core workflows: search and entity retrieval for restaurants/salons, disruption monitoring with standing queries and webhooks (register/list/delete), research submission/polling, and memory/feedback mechanisms. Minor gaps exist (e.g., no cancel for research jobs, no explicit entity list endpoint), but these are workable and do not break typical agent tasks.