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 show readOnlyHint=false and destructiveHint=false, so no contradiction. The description adds that API key is required, that answers can be JSON objects for the json_fields grader, and that finalization requires a completed data purchase. It doesn't disclose idempotency or partial-failure behavior, but it adds useful context 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 sentences, all informative: purpose, answer format, next step. No filler. The first sentence establishes the primary action and requirement; the ending routes to the next tool. Minor deduction for not front-loading the parameter semantics before the pipeline note.
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 submit tool with no output schema, the description covers the core action, prerequisites (API key), accepted answer formats, return value (accepted count), and next step. It doesn't detail error cases, validation rules, or whether answers replace existing ones, but those are less critical given the pipeline context is clear.
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%, but the description compensates by explaining answer content (scalar or JSON object for json_fields grader), and the schema itself documents run_id, answers, and agent_id. The description adds meaning to the 'answers' parameter (formats accepted, grader-specific detail) beyond what the schema provides.
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 verb ('submit'), the resource ('answers for an in-progress benchmark run'), and key details about answer formats (scalar or JSON object). It also names the next step (benchmark_finalize_run), which distinguishes this from siblings like benchmark_start_run and benchmark_get_results.
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 states API key requirement and explicitly instructs calling benchmark_finalize_run next, which gives clear usage context. It doesn't explicitly say when not to use it or name alternatives for different scenarios, but the sibling names make the pipeline position clear.
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 and data-session clusters are mostly distinct, but several tools overlap in purpose: a2awire_guide vs get_recommended_action, data_session_fund vs data_session_funding_package vs data_session_attach_escrow, and find_paid_work vs discover_agents vs hire_and_execute. The descriptions are detailed enough to disambiguate with careful reading, but an agent could easily pick the wrong funding or discovery tool.
Most tools use readable snake_case verb_noun names, and the data_session_* and benchmark_* prefixes help group workflows. However, conventions are mixed: benchmarks_get and benchmarks_list invert the usual verb_noun style compared to benchmark_get_results, and a2awire_guide is a noun rather than an action. The naming is predictable within clusters but not uniform across the whole set.
23 tools sits in the heavy range for a server whose name highlights a single benchmark, especially since many tools cover broader A2AWire platform concerns like onboarding, data sessions, job discovery, and contract verification. Each tool has a plausible purpose, but the surface feels broader than the server's stated benchmark focus requires.
The benchmark lifecycle is well covered: list, get, register, start, submit, finalize, and get results are all present, and the data-session purchase flow has the necessary open/fund/query steps. However, there are notable gaps elsewhere: find_paid_work references calling start_job, which is not exposed, and confirm_keys_persisted mentions a withdraw tool that is absent from the surface.