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?
The description adds meaningful behavioral context beyond the annotations: API key requirement, return value as accepted count, and the fact that finalization still requires a completed data purchase. It does not cover duplicate submission behavior, but the main side-effect context is disclosed.
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, each carrying necessary information: the action and auth requirement, answer shape, return value, and the next step. No filler or redundancy; the most important information is front-loaded.
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 and minimal annotations, the description provides the key workflow facts: in-progress run, accepted count, API key, and finalize prerequisite. It could be more explicit about answer_text or duplicate submissions, but an agent has enough to invoke it correctly.
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?
With only 33% schema coverage, the description compensates partially by clarifying that each answer may be a scalar or a JSON object, which helps with the 'answer' field. However, it does not clarify how answer_text relates to answer, and run_id and ordinal are only represented by their schema definitions.
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 opens with a specific verb and target: 'Submit answers for an in-progress benchmark run'. It also distinguishes this from siblings like benchmark_start_run, benchmark_finalize_run, and benchmark_get_results by focusing on the submission step.
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 clearly places the tool in the workflow: it is for an in-progress run, and it explicitly directs the agent to call benchmark_finalize_run next, including a prerequisite. It does not explicitly state when not to use it, but the sequencing is clear enough.
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 target clearly distinct resources and actions, but the benchmark cluster is confusingly close: benchmarks_get, benchmarks_list, and benchmark_get_results could be mistaken for one another despite different purposes. find_paid_work and hire_and_execute also overlap conceptually, though descriptions help separate them.
There is a general verb_noun tendency, but conventions are mixed: benchmarks_get and benchmarks_list use noun_verb order while benchmark_* tools use singular nouns; onboard_start is awkward, register is a bare verb, and a2awire_guide is a noun-only name. The inconsistency is noticeable but not chaotic.
Sixteen tools is at the upper end but reasonable for a platform covering onboarding, benchmarks, job discovery, agent hiring, earnings, and contract verification. A couple of overlapping guidance tools add mild redundancy, but most tools serve a distinct workflow.
The tool surface has significant dead ends: find_paid_work refers to start_job, register refers to confirm_keys_persisted, and benchmark finalization requires a data purchase, yet none of those tools are exposed. Agents following documented flows will fail at these missing steps.