knowledge_trust_score

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

With no annotations, the description carries the full burden and does so well: it discloses non-persistence, the saturating tanh, diminishing returns, kind-specific weighting, the never-firing lesson path, and the exists flag that distinguishes typos from neutral defaults. This is far beyond a one-line summary.

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 dense and technically thorough rather than short, but every clause adds semantic value about weighting, range, or output disambiguation. It is front-loaded with the core definition and uses structural detail effectively, despite relying on a 'see docstring' reference.

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?

For a tool with no output schema and no annotations, it fully specifies return contents (score, raw counts, exists flag), the neutral value, formula behavior, and edge cases. The only minor gap is explicit usage policy, but the invocation semantics and outcome are well covered.

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 coverage is only 50%, but the description compensates by explaining how kind changes the weighted inputs (nodes-only reads and rejected writes, lessons-only re-occurrence) and how ref maps to node/lesson references. It does not formally spell out parameter semantics, but the behavior is clear enough to invoke correctly.

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 names a concrete operation — computing a trust score — and specifies the value range, neutral default, and that it is never stored. It also explicitly distinguishes the concept from norm_rang and confidence, preventing confusion with sibling or related concepts.

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?

It gives rich context about what kind-specific weights apply and what the tool returns, but it never states when to call this tool versus other knowledge tools or when not to use it. The intended use is inferable from the behavior, but no explicit usage direction or exclusions are provided.

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