Longhand

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the fuzzy matching behavior and that it returns 'candidate projects with match reasons', which is valuable. However, it doesn't mention performance characteristics (e.g., response time), error handling, or whether this is a read-only operation (though 'returns' implies reading). For a tool with no annotations, this leaves some behavioral aspects unspecified.

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 perfectly concise and well-structured: two sentences that each earn their place. The first sentence explains what the tool does, the second provides usage context. There's zero waste or redundancy, and the information is front-loaded with the core functionality.

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 tool's moderate complexity (fuzzy matching with 2 parameters), no annotations, and no output schema, the description does well by explaining the purpose, usage, and parameter semantics. However, without an output schema, it could benefit from more detail about the return format (what 'match reasons' include, structure of candidate projects). The description is mostly complete but has a minor gap in output specification.

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 50% (only top_k has a description). The description adds significant value by explaining that the query parameter accepts 'partial project name, category, or description' - clarifying what the query parameter represents beyond just being a string. This compensates well for the schema's lack of description for the query parameter, though it doesn't provide format examples or constraints.

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's purpose with specific verbs ('fuzzy project matching', 'returns candidate projects with match reasons') and distinguishes it from siblings by explaining it's for confirming 'which game did you mean?' before drilling into episodes. It explicitly differentiates from tools like list_projects or search by focusing on fuzzy matching for project identification.

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 usage guidance: 'Useful for confirming 'which game did you mean?' before drilling into episodes.' This clearly indicates when to use this tool (for fuzzy matching to identify projects) versus alternatives like list_projects (which presumably lists all projects) or search (which might be more general). It establishes a clear context for project identification.

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