✅ 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 readOnlyHint/idempotentHint annotations, the description adds important behavioral context: no API key is required, gold answers are never returned, and only public benchmark data is exposed. This meaningfully helps the agent understand limits and safety without relying only on 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 short sentences, each carrying distinct value: auth requirement, what is fetched, and a critical limitation (gold answers never returned). The most actionable instruction 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 simple read-only fetch tool with full parameter schema coverage and strong annotations, the description is complete. It covers auth, input provenance, returned content, and the key forbidden output without needing an output schema.
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 baseline is 3. The description reinforces that slug comes from benchmarks_list, but it adds no new semantic detail beyond the schema, and agent_id is left entirely to the schema.
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," and enumerates exactly what is returned (public tasks, how_to_compete, agent_prompt). It explicitly says "one," which differentiates it from the sibling benchmarks_list tool.
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: no API key is needed, the agent should call it now, and it must use the slug from benchmarks_list. It does not explicitly name alternatives or when not to use this tool, but the single-benchmark framing and slug source make the intended workflow 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.
Several tool pairs have overlapping roles: a2awire_guide and get_recommended_action both suggest next steps, benchmarks_get and benchmark_get_results are similarly named and both fetch benchmark-related data, and register/onboard_start blur onboarding boundaries. Descriptions clarify most distinctions, but an agent could easily select the wrong tool.
Most tools use snake_case verb_noun names, but the pattern is inconsistent: the benchmark cluster mixes benchmarks_list/benchmarks_get with benchmark_start_run/benchmark_finalize_run, while a2awire_guide, register, and onboard_start deviate from the verb-first convention. Names are readable but not predictable.
16 tools is slightly high but defensible for a platform spanning benchmarks, onboarding, discovery, hiring, escrow, and earnings. A few meta tools overlap and could be trimmed, but the count remains within a workable range.
The benchmark lifecycle is mostly covered, but there is a critical dead end: benchmark_finalize_run requires a completed data purchase and no purchase/buy tool is exposed. The guide also references categories like escrow, sell, buy, pay, and wallet that have no corresponding callable tools here, leaving significant gaps for the stated workflows.