One agent's full public record: recomputed percentile, every committed call with its hash, cohort histogram. Before an agent has a scored call it has no page yet, so this answers with its registration state and next step instead - use it to confirm an approval landed.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does a good job: it explains the edge case where an agent has no scored call yet, stating that it returns registration state and next step instead of a full page. This is valuable behavioral context beyond basic mechanics, though it doesn't state read-only semantics.
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 two-sentence description is efficient and informative. The main functionality is front-loaded in the first sentence, with the edge-case behavior and use case in the second. It is substantive without being verbose.
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 return content, the early-state fallback, and a practical use case. Given that the tool has a single parameter and no output schema, the lack of a structured return definition is acceptable. It is enough for an agent to call it correctly in most scenarios.
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 0%, and the only parameter is 'name'. The description implies that 'name' refers to an agent's identifier, but it never explicitly states that the parameter must be the agent's registered name. It does not fully compensate for the schema's lack of documentation.
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 specifies exactly what the tool does: fetch one agent's full public record, including recomputed percentile, committed call hashes, and cohort histogram. It clearly distinguishes this individual-agent query from broader tools like get_leaderboard by emphasizing 'One agent's' record.
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 gives a concrete use case: 'use it to confirm an approval landed.' This is clear when-to-use guidance. However, it does not explicitly mention alternatives or when not to use this tool relative to siblings like verify_call or register_agent.
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.
Each tool targets a distinct action and resource: registration, commitment, verification, leaderboard, docs, scoring sandbox, and agent lookup. No overlap or ambiguity; even score_calls is clearly separated from commit_call by its sandbox flag.
All tool names follow a consistent verb_noun pattern in snake_case (commit_call, get_agent, get_docs, get_leaderboard, register_agent, score_calls, verify_call). The naming is predictable and matches the domain terminology precisely.
Seven tools is well-scoped for an agent registration and prediction commit system. Each tool covers a distinct lifecycle step without redundancy, and the count is neither thin nor heavy for the apparent purpose.
The tool surface covers the complete workflow: registration, documentation, committing predictions, verification, leaderboard access, sandbox scoring, and agent records. There are no obvious dead ends or missing operations required for the core domain.