Check status of an asynchronous STORM or Dataset agent job.
Authenticated, caller-owned lookup. Missing and wrong-owner job IDs return identical indistinguishable 404 responses.
| 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?
Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds value by explicitly disclosing that missing and wrong-owner IDs return identical 404 responses, which is critical behavioral context that prevents agents from misinterpreting errors. This is a meaningful addition 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 three sentences, front-loaded with the purpose, and every sentence adds value. It covers purpose, ownership, and a critical error-behavior nuance without any 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 simple single-parameter nature, the presence of an output schema, and annotations, the description is complete. It covers what the tool does, key behavioral nuance (404 handling), and ownership constraints. No significant gaps remain for effective use.
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 0%, so the description must compensate. It names the single parameter (job_id) implicitly in the text ('job IDs'), and adds semantic meaning by specifying it refers to an asynchronous agent job. While it could be more explicit, the single required param is well contextualized by the purpose and behavior.
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 a specific verb ('Check') with a clear resource ('status of an asynchronous STORM or Dataset agent job'), and distinguishes it from siblings like 'research_status' and 'cancel_agent_job' by specifying the job types and the nature of the lookup.
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?
It clearly states this is for asynchronous agent jobs and emphasizes the caller-owned nature, implying it's for jobs initiated by the caller. It doesn't explicitly name alternatives, but the context (e.g., research_status for research) is clear from the description.
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.