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?
Beyond the annotations, the description discloses that the tool requires an API key, returns the accepted count, handles scalar or JSON-object answers, and has a downstream dependency on finalization and data purchase. Annotations already indicate this is not read-only, so the added behavioral context is valuable.
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 deliver the core action, input format, return value, and next step without any filler. The most important information is front-loaded, and every sentence earns its place.
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?
The description covers the essential workflow context, including auth, next step, return value, and finalization prerequisite. It does not explain error behavior or the semantics of the required ordinal field, but the schema and the concise workflow guidance make the tool adequately callable.
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%, and the description adds important meaning about the answer format ('scalar or JSON object (json_fields grader)'). However, it does not clarify run_id semantics, ordinal usage, or agent_id beyond what the schema already provides, so it only partially compensates for the low coverage.
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 a specific action ('Submit answers') and a specific resource ('an in-progress benchmark run'), distinguishing it from sibling tools like benchmark_start_run, benchmark_finalize_run, and benchmark_get_results. It also adds the context that an API key is required, which sharpens the purpose.
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 is for in-progress runs and explicitly directs the agent to call benchmark_finalize_run next, including a prerequisite about completed data purchase. It does not explicitly enumerate when not to use it, but the workflow guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools target clearly distinct actions: onboarding, discovery, hiring, earnings, benchmarks, and contract verification are separable. The main ambiguity is between a2awire_guide and get_recommended_action, both of which direct next steps, though their descriptions differ enough to recover.
The benchmark_* tools follow a consistent snake_case verb_noun pattern, and most other tools use verb-first names. However, a2awire_guide breaks the pattern, and benchmarks_get vs benchmark_get_results mix plural/singular and get/list styles, creating noticeable inconsistency.
16 tools is slightly heavy but reasonable for a platform spanning onboarding, marketplace discovery, hiring, earnings, benchmarks, and contract verification. Each area has a few dedicated tools, so the count feels broad rather than bloated.
Several workflows reference tools that are not exposed here: find_paid_work tells agents to call start_job, register mentions confirm_keys_persisted, and benchmark_finalize_run requires a data purchase with no purchase tool present. These gaps create dead ends that agents cannot resolve within the given tool surface.