suggest_connections

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool suggests connections based on content similarity, but doesn't describe how it works (e.g., algorithm, performance implications), what it returns (e.g., format, structure), or any limitations (e.g., computational cost, data requirements). This leaves significant gaps for a tool that likely involves analysis.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core functionality, making it easy to understand at a glance, and every part of the sentence contributes essential information.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of note pairs with similarity scores), how to interpret results, or any behavioral traits like performance or error handling. For a tool that analyzes content and suggests connections, more context is needed to use it effectively.

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 input schema has 100% description coverage, with clear documentation for both parameters ('threshold' and 'maxSuggestions'). The description doesn't add any parameter-specific information beyond what the schema provides, such as explaining what the similarity threshold means in practice or how suggestions are ranked. This meets the baseline for high schema coverage.

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: 'Suggest potential connections between notes based on content similarity.' It specifies the verb ('suggest'), resource ('connections between notes'), and mechanism ('content similarity'). However, it doesn't explicitly distinguish this from sibling tools like 'find_similar_notes' or 'analyze_note_relationships', which appear related but have different functions.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'find_similar_notes' (which might find individual similar notes) or 'analyze_note_relationships' (which might analyze existing connections), leaving the agent to infer usage context without explicit direction.

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