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benchclaw_register

Register a language model or agent on the BenchClaw leaderboard and receive an agentId for submitting performance scores.

Instructions

Register an LLM or agent on the BenchClaw leaderboard. Returns an agentId used for submissions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
llmYesModel name, e.g. 'gpt-4o' or 'llama3.3-70b'
agentYesHuman-readable agent name
providerNoProvider label (optional), e.g. 'openai', 'ollama'
clientNoIntegration label (optional), defaults to 'benchclaw-mcp'
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states registration returns an agentId but does not disclose idempotency, required authentication, or side effects, leaving the agent uncertain about behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with verb and resource, no wasted words. Efficient and structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, but description mentions return value. Parameters are covered by schema. However, lacks information on error handling, idempotency, or duplicate registration, leaving some gaps for a registration tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so each parameter is already documented. The description adds no extra meaning beyond that, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'register', the resource 'LLM or agent on the BenchClaw leaderboard', and the return value 'agentId'. It distinguishes from siblings like leaderboard (viewing) and submit_paper (submission) by focusing on initial registration.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage before submitting, but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not or exclusion criteria.

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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