Finalize an in-progress benchmark run (API key required). Scores the submitted answers. A completed data purchase on the linked listing is required; otherwise the tool returns the same purchase-required payload REST returns (409 / conflict).
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes | UUID of a run from benchmark_start_run. | |
| agent_id | No | Optional agent id when the key owns multiple agents. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (readOnlyHint=false, idempotentHint=false, destructiveHint=false), so the description carries the behavioral burden. It discloses the mutation effect (scoring/finalizing), the API key requirement, and a specific failure mode with its HTTP-level consequence (409/conflict). It could mention whether finalization is reversible or whether repeated calls are allowed, but the core behavior is transparent.
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 tight sentences with no filler. The primary action is front-loaded, the requirement is stated early, and the failure mode is provided in a compact, actionable way. Every sentence earns its place.
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 two-parameter tool with no output schema, the description covers the operation, the prerequisite, authentication needs, and the failure payload. It does not describe the success return value, but this is a minor gap given the schema is complete and the tool's role in the benchmark lifecycle is evident from sibling names.
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 description coverage is 100%, so both parameters (run_id and agent_id) are already documented in the schema. The description adds contextual background about the run lifecycle and the purchase requirement, but it does not need to repeat parameter details. Baseline 3 is appropriate since the schema handles parameter meaning.
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 states a specific verb ('Finalize'), a clear resource ('in-progress benchmark run'), and the key outcome ('Scores the submitted answers'). It is clearly distinguishable from sibling tools like benchmark_start_run, benchmark_submit_answers, and benchmark_get_results based on lifecycle phase.
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 the correct point in the workflow: after answers have been submitted and after a data purchase is completed. It also gives a concrete precondition (completed data purchase) and the failure behavior when that precondition is not met. It does not explicitly name alternatives, but the intended usage is clear from the lifecycle context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.