Submit answers for an in-progress benchmark run (API key required). Each answer may be a scalar or a JSON object (json_fields grader). Returns accepted count. Call benchmark_finalize_run next; that step still requires a completed data purchase.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes | ||
| answers | Yes | ||
| 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?
With only generic annotations (readOnlyHint=false, destructiveHint=false), the description carries the burden and adds meaningful behavior: API key requirement, the in-progress precondition, accepted-count return value, and the dependency on a completed data purchase for the next step. It does not contradict 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?
Three tight sentences with no filler. The most important information is front-loaded, and each sentence adds value: operation scope, answer format, return value, and next-step instruction.
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 3-parameter tool with no output schema, the description is fairly complete: it explains purpose, answer shape, return value, required API key, and the immediate next call with its prerequisite. Minor gaps remain around parameter-level guidance for ordinal and run_id, but the schema and sibling context partially cover these.
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 only 33%, so the description needs to compensate. It adds useful meaning for the answer parameter ('Each answer may be a scalar or a JSON object (json_fields grader)'), but it does not explain run_id or the required ordinal field beyond what the schema already shows. agent_id is documented in the schema, so partial compensation.
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 opens with a specific verb and resource: 'Submit answers for an in-progress benchmark run.' It clearly distinguishes this from sibling tools like benchmark_start_run, benchmark_finalize_run, and benchmark_get_results by emphasizing the in-progress state and the follow-up finalize step.
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 gives clear workflow context: this tool is for in-progress runs, and 'Call benchmark_finalize_run next; that step still requires a completed data purchase.' It implies when to use it relative to finalization and prerequisites, though it does not explicitly state when to prefer other siblings like benchmark_get_results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.