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 provide only generic mutation hints, so the description adds meaningful behavioral context: API key requirement, accepted count return, support for scalar or JSON-object answers, and a dependency on a completed data purchase for the next step. This goes beyond annotations and helps the agent anticipate preconditions and result shape.
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 sentences with high information density: the main action, key constraints, return value, and next step are all covered without redundancy. The workflow is front-loaded and every sentence adds operational value.
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 3 parameters, no output schema, and minimal annotations, the description provides the critical operational context: when to call it, what auth is needed, what the answer format is, what it returns, and what to do next. It could additionally clarify duplicate handling or partial acceptance behavior, but the essential invocation context is present.
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 does explain the answer value format (scalar or JSON object for json_fields grader), but it does not clarify run_id semantics, ordinal meaning, answer_text vs. answer, or agent_id beyond the minimal schema note. Partial compensation is present, but some parameter meaning remains underspecified.
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 clearly states the action ('Submit answers'), the resource ('benchmark run'), and the state requirement ('in-progress'). It also distinguishes itself from siblings by referencing the follow-up step (benchmark_finalize_run), making the tool's role in the workflow unambiguous.
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 context: use this for an in-progress run, after answers are ready, before benchmark_finalize_run. It does not explicitly list when-not-to-use scenarios or compare against sibling tools like benchmark_get_results, but the workflow guidance is strong enough for an agent to route 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 tools fall into distinct categories: onboarding, benchmarks, marketplace, earnings, and contract verification. A2AWire guide and get_recommended_action have some meta-guidance overlap, but their descriptions clarify one is a catalog and the other is a state-based next-step recommendation.
Most tools use an imperative verb-noun pattern (register, check_earnings, discover_agents, hire_and_execute), but the benchmark tools are inconsistent: benchmarks_get/benchmarks_list vs benchmark_start_run/benchmark_finalize_run mix plural prefixes and verb placement. a2awire_guide and onboard_start also break the dominant pattern.
16 tools is borderline-heavy but arguably acceptable for the broad A2AWire marketplace/benchmarking scope. However, the server name 'Production Schedule Optimizer' does not match the tool surface at all, which makes the count feel arbitrary and poorly aligned.
Several descriptions reference tools that are not actually exposed, such as start_job and confirm_keys_persisted, creating dead ends for agents. The set also lacks job completion, update/cancel, or escrow management operations, leaving the lifecycle incomplete.