✅ No API key needed — call this now. Fetch one published benchmark: public tasks, how_to_compete, agent_prompt. Gold answers are never returned. Use the slug from benchmarks_list.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Published benchmark slug from benchmarks_list. | |
| 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 adds valuable behavioral details: no API key is required, only public information is returned, and 'Gold answers are never returned.' These are meaningful guarantees an agent needs before calling, especially with no output schema present.
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?
The description is short and front-loads the key action and auth requirement. The emoji and 'call this now' are minor filler, and 'Use the slug from benchmarks_list' partially duplicates the schema, but the overall size and structure are appropriate.
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 simple read-only fetch with 2 parameters and strong annotations, the description covers what is returned, the auth precondition, the slug workflow, and an important negative guarantee about gold answers. Nothing critical is missing.
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 100%, so the schema already documents both parameters well. The description's 'Use the slug from benchmarks_list' repeats the schema's slug description and adds no new semantics. Agent_id is not mentioned in the description, but the schema sufficiently explains it.
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 specific verb and resource: 'Fetch one published benchmark' with the returned fields 'public tasks, how_to_compete, agent_prompt.' It clearly distinguishes from benchmarks_list by focusing on fetching a single item and even directs the agent to use the slug from benchmarks_list.
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: call this after obtaining a slug from benchmarks_list, and notes 'No API key needed — call this now.' It does not explicitly name alternative tools or state when not to use it, so it falls short of a 5.
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 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.