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 indicate non-read-only and non-idempotent behavior, so the description adds useful context: API key requirement, accepted count as the return value, and the data-purchase prerequisite for finalization. It does not elaborate on duplicate submission effects, but the provided operational details go beyond the 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 tightly worded sentences front-load the core action and state, then add answer-format guidance, return behavior, and the required next step. No sentence is redundant or filler.
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?
No output schema exists, but the description discloses accepted count and the critical finalize-next requirement. It also captures the answer format nuance. It does not address possible error cases or how answer_text relates to answer, but the workflow context is sufficiently complete for correct 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 explains the flexible 'answer' format and the json_fields grader case, but it does not clarify run_id, the ordered answers array, ordinal semantics, or answer_text. The schema covers some of this, but the description leaves a noticeable parameter-meaning gap.
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 clear verb ('Submit'), a specific resource ('answers for an in-progress benchmark run'), and the key characteristic that answers may be scalars or JSON objects. It also names the next workflow step, distinguishing it from finalization or retrieval tools.
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?
It states the prerequisite of an in-progress run and that an API key is required, and instructs the caller to invoke benchmark_finalize_run next. It does not explicitly enumerate when not to use alternatives, but the sequencing and run-state condition give clear practical guidance.
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.
Most tools have distinct resources and actions, and the benchmark lifecycle is clearly separated into list/get/start/submit/finalize/results. A few pairs could cause hesitation — a2awire_guide vs get_recommended_action both offer next-step guidance, and get_agent_contract vs verify_contract sound similar despite different targets.
Snake_case verb_noun naming dominates, e.g. check_earnings, discover_agents, find_paid_work, hire_and_execute. However, benchmarks_get and benchmarks_list reverse the verb/noun order, a2awire_guide lacks a verb, and onboard_start reads more like a status than an action.
16 tools cover a broad but coherent scope: onboarding, benchmarks, jobs, hiring/escrow, earnings, and contract verification. This is slightly above the ideal 3-15 band, and a few meta-tools like a2awire_guide and get_recommended_action make it feel heavier, but no tool is egregiously redundant.
The benchmark workflow is fairly complete, but the paid-work and onboarding flows have critical gaps: find_paid_work explicitly tells agents to call start_job, which is not provided, and register references confirm_keys_persisted, which is also absent. This means agents can find work but cannot actually start or complete the sell-side workflow.