get_run_reportRetrieve a test run's AI QA report with verdict, impact findings, fixes, next steps, and untested areas to decide whether to ship, ship with care, or hold.
Read-only. The AI QA report for a run: verdict (ship, ship with care, hold), findings with impact and fixes, next steps, what was not tested.
| Name | Required | Description | Default |
|---|---|---|---|
| run | Yes | Run ref (e.g. payments-kqzxm) or numeric id |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly/idempotent/non-destructive, so 'Read-only' is partly redundant, but the description earns credit by disclosing the return shape that no output schema provides: the verdict vocabulary (ship, ship with care, hold), findings carrying impact and fixes, and explicit 'what was not tested' coverage. It omits whether the report may be absent or still generating, which is a real behavioral gap given wait_for_run exists as a sibling.
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?
A single compact sentence set, front-loaded with the safety note and then the payload contents; every clause carries information. The leading 'Read-only.' duplicates the annotation and is the only real waste.
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?
With no output schema, the description carries the return-value burden and does so well by naming the verdict enum and finding structure. For a one-parameter read tool with full annotations this is nearly complete; the missing piece is availability semantics — whether a report always exists or may need wait_for_run first.
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 single 'run' parameter is fully documented in the schema (coverage 100%), including the ref-or-numeric-id format and examples. The description adds nothing about parameter semantics, so the baseline 3 for high schema coverage applies.
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?
Names a specific resource ('the AI QA report for a run') and enumerates its contents — verdict, findings with impact and fixes, next steps, untested areas — which cleanly separates it from raw-data siblings like get_run_results and list_findings. It stops short of explicitly naming or contrasting those siblings, so an agent must infer the boundary itself.
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?
Usage is implied by the resource and the enumerated contents: fetch this when you want the synthesized verdict rather than raw results. However, the description never states when to prefer this over get_run_results, list_findings, or get_run, nor whether a report is guaranteed to exist for a run — the reader has to infer all of that.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.