causal_map

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

Annotations already indicate readOnly and idempotent behavior. The description adds context that it's an aggregated, non-destructive view, which is consistent. No contradictions.

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: first defines the core function, second adds scoping. Front-loaded, no fluff. Highly efficient.

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 output schema exists, the description sufficiently covers what the tool does and when to use it. For a read-only data retrieval tool, it's complete.

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 only 33%, but the description explains the purpose of tag and problem_id for scoping, adding value beyond the schema. emit_canvas is described in the schema. This compensates for the low 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 that the tool provides an aggregated view of intervention effects on diagnostic subgraphs across experiments, acting as a meta-analysis. It uses specific terms like 'Δdiagnostic subgraph' and 'mini meta-analysis', distinguishing it from sibling tools like register_diagnostic or list_interventions.

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 implies usage for meta-analysis of intervention effects and explicitly mentions scoping by problem_id or tag. It lacks explicit when-not-to-use or alternative tools, but the purpose is clear enough for a knowledgeable agent.

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