get_report_resultFetch a completed deep research report by slug. Free. Returns the long-form report (markdown), a structured-findings JSON block, the action list, and the report card. If the report is still generating you get its status instead; if it failed, the launch charge is refunded automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The slug returned by start_deep_report |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description fully aligns with the readOnlyHint annotation and adds valuable context: conditional behavior when generating (returns status) and refund on failure. This goes beyond what annotations provide.
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?
Three sentences, front-loaded with core action and price, no filler. Each sentence adds essential information.
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 tool with one parameter and no output schema, the description covers inputs, outputs, conditional behavior, and pricing. It is complete enough, though some terms like 'action list' could be elaborated.
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?
With only one parameter and 100% schema coverage, the description adds marginal value by linking the slug to start_deep_report. The schema's description is already adequate, so the baseline 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?
The description clearly states the tool fetches a completed deep research report by slug. It lists the specific return components, distinguishing it from sibling tools like check_report_status and start_deep_report.
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 implies when to use (after report completion or to get status) and mentions the automatic refund on failure. However, it does not explicitly contrast with check_report_status for status-only queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool serves a distinct function: fact verification, visibility check, review gap analysis, competitor scan, and deep research. The report-related tools (start, poll, fetch) are clearly separated by their roles. No two tools are easily confused.
All names use snake_case consistently. Most start with verbs like 'check_', 'get_', 'run_', or 'start_', though 'ping' and 'review_gap' deviate slightly. The pattern is predictable and readable.
Eight tools cover the domain of local business intelligence well: fact-checking, visibility, review comparison, competitor scan, and deep research. The count is neither too sparse nor overwhelming for the scope.
The tool surface covers core intelligence needs: fact verification, review comparisons, visibility checks, competitor scans, and deep research reports. Minor gaps like a tool to list businesses or manage multiple entities exist, but for single-business analysis it's nearly complete.