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?
Annotations only provide generic false hints, so the description carries the burden of behavioral disclosure. It adds meaningful context: API key authentication, accepted answer formats (scalar or JSON object for json_fields grader), and a returned accepted count. It also discloses a downstream dependency (completed data purchase for finalize), which helps the agent anticipate state requirements.
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?
The description is compact and well-organized. The main action and authentication requirement are in the first sentence; the answer format and return value follow; the next-step handoff is last. Every sentence carries useful information without fluff or repetition.
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 tool with no output schema, the description sufficiently covers the return value (accepted count), prerequisites (API key, in-progress run), and the immediate next step. The main gap is the lack of explanation for the item-level answer fields (e.g., answer vs answer_text) and how to obtain or specify the run_id, but the description is otherwise adequate for normal invocation.
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 must compensate. It does add useful meaning about the 'answers' parameter by explaining that each answer can be a scalar or JSON object (json_fields grader). However, it does not clarify run_id format/origin or agent_id selection beyond what the schema already provides, and it remains ambiguous how answer maps to the item's answer/answer_text fields.
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 uses a specific verb and resource: 'Submit answers for an in-progress benchmark run.' It clarifies the operation type and the run state, and distinguishes the tool from siblings like benchmark_finalize_run by framing it as the answer-submission step. It also adds key constraints (API key required) and expected return behavior.
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 usage context: it targets in-progress benchmark runs and positions the tool as the step before benchmark_finalize_run. It explicitly names the next tool and notes an additional prerequisite for that next step. It does not list alternative tools or exclusion criteria, but the sequencing guidance is strong enough for an agent to choose correctly.
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.
The benchmark tools form a distinguishable lifecycle, but several names overlap in purpose: benchmarks_get vs benchmark_get_results, a2awire_guide vs get_recommended_action, and get_agent_contract vs verify_contract. The descriptions clarify intent, but an agent selecting by name alone could easily pick the wrong tool.
Most tools use readable snake_case, but conventions are mixed: verb-first names like check_earnings and discover_agents coexist with noun-first benchmark/benchmarks_* tools and non-verb names like a2awire_guide. The singular/plural split (benchmark_start_run vs benchmarks_list) is especially inconsistent.
Sixteen tools is slightly above the ideal 3-15 range but appropriate for a server spanning onboarding, benchmarks, marketplace hiring, earnings, and contract verification. Each tool covers a distinct step and none feel purely decorative.
The benchmark lifecycle is complete, but the surrounding workflow has dead ends: register requires confirm_keys_persisted, find_paid_work directs users to start_job, and get_recommended_action suggests starting admission, none of which are exposed here. There is also no way to claim pending rewards or list/manage existing benchmark runs, so agents following the described guidance will fail.