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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It covers: input formats (wallet or token_prefix), output fields (composite, sub-metrics, trust grade, flags, wallet age, etc.), rebuild schedule (daily at 04:50 UTC), data source (TF's own telemetry), and error handling (ok=false for unknown). All critical behavioral aspects are transparent.

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 a single paragraph of about 120 words, front-loaded with the primary purpose. Every sentence adds value: input constraints, output details, schedule, error handling, and use cases. There is no wasted text, and the structure is logical and easy to follow.

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?

Given the absence of an output schema and annotations, the description comprehensively covers all necessary context: input requirements, output structure (listing all major components), data freshness, error behavior, and practical use cases. It leaves no critical gaps for an agent to select and invoke the tool 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?

Since the input schema already describes both parameters with 100% coverage (wallet: 'EIP-55 or lowercased EOA wallet address (0x + 40 hex). Mutually exclusive with token_prefix.'; token_prefix: 'First 16 chars of a tf_live_ bearer token (e.g. "tf_live_18e54f47"). Mutually exclusive with wallet.'), the tool description adds minimal additional semantic value beyond restating these details. Baseline 3 is appropriate.

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 clearly states the tool looks up an agent's public reputation card from the TensorFeed Agent Reputation Bureau. It specifies the input types (wallet or token_prefix) and the output structure in detail. Although it does not explicitly compare to sibling tools, its unique function (agent reputation lookup) is distinct from all listed siblings (e.g., check_ai_supply_chain_risk, get_agent_opportunities), making the purpose unambiguous.

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 provides explicit use cases: marketplaces routing work, peer agents deciding trust, ops dashboards, and operators inspecting their own reputation. It also clarifies the error handling (ok=false for unknown identities), which helps callers handle failures. However, it does not explicitly state when not to use the tool or mention alternative tools, which would strengthen the guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.