suggest_knowledge_for_design

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

There are no annotations, so the description bears the full burden of behavioral disclosure. It explains the internal mechanism (SECTION_KNOWLEDGE_MAP, FTS5, lineage) and the return format, which is helpful. However, it does not explicitly state whether the operation is read-only or whether there are side effects, permissions, or prerequisites. Since the tool name suggests a read-only action, the omission is notable but not misleading.

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 well-structured with a brief summary, a short explanation of the matching process, an Args list, and a Returns line. It uses information-dense language without redundant fluff, making it easy to parse. The format is appropriate for an API reference.

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 general purpose and parameter roles, and the existing output schema helps define return values. However, with all parameters optional and no required fields, it does not specify the default behavior when no arguments are provided (e.g., does 'section' default to something, or does an empty filter return all knowledge?). This missing edge-case behavior leaves an important gap for agent decision-making.

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%, yet the description documents all five parameters with meaningful details. It provides an example for 'section', explains the format for 'source_ids' (comma-separated), clarifies 'theme_id' matching, and describes 'hypothesis_text' and 'parent_id' with their matching strategies. This fully compensates for the lack of schema descriptions.

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 states a specific verb ('Suggest') and resource ('knowledge entries') with a clear scope ('relevant to a design section'), which makes the tool's function immediately understandable. It also differentiates from the sibling 'suggest_cautions' by focusing on knowledge entries rather than cautions.

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 clear context about when to use the tool: when knowledge entries related to a design section are needed. It does not explicitly mention exclusions or alternatives, but the context is sufficient for an agent to infer the appropriate scenario. No explicit when-not guidance is given, so it falls short of a 5.

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