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 show readOnlyHint and idempotentHint. Description adds polling behavior and expected statuses. No contradictions, but could mention behavior after completion or error handling.
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?
Extremely concise: one purpose sentence plus clear usage directive. No redundant wording.
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 polling tool with one parameter and no output schema, the description covers key aspects: purpose, trigger, polling cycle, and end states. Could mention result format but not critical.
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 job_id description linking to get_supplemental_context. Description reiterates that but adds no new parameter details. Baseline of 3 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?
States specific action ('check if market data gathering is complete and retrieve the results') and distinct resource (supplemental context). Context of use after get_supplemental_context differentiates from siblings like check_research_status.
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 instructs when to call ('after get_supplemental_context'), how to use (poll every 5-10 seconds), and termination conditions (COMPLETE or FAILED).
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.
Most tools have clearly distinct roles: graph discovery, graph search, node detail, neighbor mapping, evidence retrieval, deep research launch, and status polling are all identifiable. The main ambiguity is between search_graph and get_label_values(label='Trend') for listing trends, and between deep_research_topic's built-in supplemental coverage and get_supplemental_context.
The set largely follows a snake_case verb_noun pattern such as search_graph, list_graphs, get_node, read_url, and generate_visual. The outlier is deep_research_topic, which is noun-led rather than verb-led, and get_my_account is a minor deviation from the pure verb_noun pattern, but neither seriously disrupts usability.
14 tools is well-scoped for a deep-research platform: graph discovery, graph search, retrieval, evidence, supplemental data, status polling, visualization, URL ingestion, and account/capability helpers all earn their place. The set feels like a deliberate pipeline rather than a miscellany.
The research lifecycle is well covered: discover graphs, search, explore trends, get supporting evidence, add supplemental data, launch deep research, poll status, and generate visuals. Minor conveniences like canceling or listing past research sessions are missing, but there are no dead ends for the core workflow.