credence_self_probe

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

With no annotations, the description discloses zero external calls, no model judgment, and unverified registration. It could mention if any state is persisted or side effects, but the transparency is strong for a simple registration tool.

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 three sentences, front-loaded with the core action, and no wasted words. Every sentence adds value.

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?

Despite having an output schema (not mentioned), the description covers purpose, behavior, usage context, and next steps (credence_verify). It is complete for the tool's complexity.

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 covers both parameters with clear descriptions (code and session_id). The description adds no new semantics beyond the schema, so a baseline score of 3 applies.

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 extracts domain-relevant values from generated code and registers them as unverified, with no API calls or model judgment. It distinguishes itself from the sibling credence_verify by specifying that verification is a separate step.

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 defines the use case: after generating code with any coding agent, extract values and register them unverified. It implies when to use by contrasting with zero API calls and zero model judgment, but does not explicitly list when not to use.

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