Check if market data gathering is complete and retrieve the results. Call this after get_supplemental_context — poll every 5-10 seconds until status is COMPLETE or FAILED.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The Job ID returned by get_supplemental_context |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds value by explaining polling behavior and expected status outcomes, which is not covered by 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?
Two sentences are concise and front-loaded with purpose, then usage instructions. No unnecessary 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?
Despite no output schema, description explains return status meanings. With one required parameter and clear usage context, the description is fully 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?
Only one parameter 'job_id' with description in schema. Schema coverage is 100%, so description adds minimal extra meaning. Baseline score 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?
Description clearly states the tool checks completeness of market data gathering and retrieves results, using specific verb 'check' and resource 'supplemental status'. It also distinguishes from sibling tool get_supplemental_context by stating to call after it.
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?
Explicitly specifies when to use the tool: after get_supplemental_context, with polling interval of 5-10 seconds until COMPLETE or FAILED. No explicit exclusions or alternatives, but context is clear.
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.
The tools are mostly distinct: search_graph, search_insights, search_statistics, and get_supplemental_context are carefully differentiated by output type, and graph exploration/evidence tools have clear roles. A couple of retrieval tools (get_validated_trends vs search_graph, search_statistics vs get_supplemental_context) could be mistaken at a glance, though their descriptions do enough to separate them.
All tool names use a consistent lowercase snake_case verb_noun pattern (search_*, get_*, list_*, generate_*, check_*, read_*). Verbs map predictably to actions, and there are no mixed conventions or vague generic names.
Fifteen tools is at the upper edge of the ideal range but each one maps to a distinct research workflow step: discovery, graph search, targeted retrieval, evidence, supplemental data, visualization, and account/capability checks. The breadth is justified by the server's broad trend-research scope.
The surface covers the full read-only research lifecycle: list graphs, search across them, drill into nodes/neighbors/evidence, get quantitative and qualitative answers, supplement thin coverage with external data, and produce visuals. Meta tools for account/capability and URL import prevent dead ends.