search_similar_issues

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

The description discloses the ranking mechanism ('exact cosine similarity over sentence-embeddings') and implies read-only behavior (searching issues). There are no hidden side effects or destructive actions. Since no annotations are provided, the description fully carries the transparency burden and does so effectively.

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 concise (two sentences) and front-loaded with the primary purpose. It then provides implementation detail and a usage example. Every sentence adds value, with no fluff or redundancy.

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 what the tool does, how it ranks results, and when to use it, including a concrete example ('cite the returned issue numbers'). It is self-contained for an agent to decide and invoke appropriately, despite not specifying the return format, which is not essential for a search tool.

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?

The description does not mention parameters 'query' or 'k,' but the input schema already fully defines them with types and a default for 'k.' Since schema coverage appears complete, the description adds no additional meaning beyond the schema, warranting a baseline score of 3.

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: finding issues semantically similar to a free-text query. It uses the specific verb 'Find' and resource 'issues,' making it unambiguous. It also implies differentiation from sibling tools by focusing on semantic similarity rather than other operations like duplicate detection or triage.

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 a direct use case: 'Use this to answer "has anything like this been reported?"' This gives clear guidance on when to apply the tool. It does not explicitly contrast with sibling tools, but the stated scenario is specific enough to guide appropriate usage. A minor gap is the lack of explicit 'when not to use.'

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