why

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

The description discloses the full behavioral scope: it returns matching decisions with rationale, rejected alternatives, context, evidence from memories, and supersession. It also notes the effect of the 'limit' parameter. However, with no annotations, it does not explicitly state whether the operation is read-only or lacks side effects, though the nature of the query strongly implies it. This is strong transparency but not exhaustive.

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 front-loaded with a purpose statement, followed by a concise example of usage and return content, then the parameter list. There is no fluff or repetition; every sentence contributes value. The formatting is clean and easy to scan.

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 tool is complex (decision provenance with multiple output components), but the description covers the input semantics, the output structure, and the scope controls. An output schema exists, but the description goes beyond that to explain the meaning of the results. No critical information is missing.

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 includes an Args block that explains each parameter in plain language: 'question' is a natural-language why-question, 'project' scopes to a project, and 'limit' caps the number of decisions. Since the schema provides no descriptions for these parameters, this is essential and fully compensates, exceeding the baseline.

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 as 'Interrogate decision provenance' and provides a concrete example ('why do we use X and not Y?'). It specifies the return payload (decisions, rationale, rejected alternatives, context, evidence, supersession), which distinguishes it from sibling tools like recall or list_decisions.

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 gives a clear scenario for when to use the tool: when asking a 'why' question about decisions. It does not explicitly mention alternatives or exclusions, but the example and problem domain make the intended usage obvious. No 'when not to use' guidance is provided, 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.