find_causes

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

No annotations provided. Description explains that the tool searches paths and sorts by score, but does not disclose whether it modifies data or any side effects. Given no readOnlyHint, this is adequate but not explicit.

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?

Two sentences front-loaded with identifier and purpose. No redundant information; every word earns its place.

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 complexity of causal chain search and no output schema, the description does not specify the return format (e.g., list of paths with scores and edges). It is functional but leaves ambiguity about result structure.

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?

With 67% schema coverage, description adds significant meaning: explains the causal relation types used, the scoring method (product of causalStrength), and implies sorting. This compensates for undocumented maxDepth and clarifies the purpose of candidates.

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?

Description clearly states 'Find causal chains ending at the named effect', specifying verb and resource. It further distinguishes from siblings by detailing the search approach (via causal relation types) and sorting mechanism (product of causalStrength).

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?

No guidance on when to use this tool vs alternatives like find_effects, detect_causal_cycles, or find_all_paths. The agent is left to infer context from the description alone.

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