Score an entity from known contributors

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

No annotations are provided, so the description carries the full behavioral burden, and it does substantial work: it discloses the weighting model (tier x source x proof-strength x reputation x recency decay), the effective-sample-size computation, and the shrinkage-toward-prior behavior with thin vs. deep evidence. It also notes the empty-signals edge case yields the prior. It stops short of stating what the response actually contains or any determinism caveat (the `now` param hint lives in the schema, not here).

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?

Front-loaded with the verb and scope, then the mechanism, then the routing rule — a sensible order with no filler sentences. It is dense and somewhat long-winded in the mechanism sentence, but each clause carries distinct information.

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 complex, nested-schema tool with no output schema and no annotations, the description explains the computation well but never says what the tool returns (score, confidence band, effective sample size?). The config's confidence thresholds imply a confidence output, yet the return shape is left entirely to inference.

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 100%, so baseline is 3, but the description adds conceptual meaning the per-param descriptions do not: it explains that `dial` is the shrinkage strength and that `prior` is the baseline thin evidence shrinks toward, framing how the two required params interact. It does not add anything for `signals` item fields beyond the schema's own vocabulary guidance.

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?

States a specific verb+resource ('scores one entity or one dimension') and scopes it to signals from KNOWN, identified sources. It explicitly distinguishes itself from the sibling assess_anonymous_authenticity by naming the unattributed/scraped case, so an agent can route without opening a schema.

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?

Gives an explicit use case ('trust/reputation scores backed by attributable evidence') and an explicit exclusion with the alternative tool named ('for unattributed/scraped signals ... use assess_anonymous_authenticity instead'). The condition that selects each tool is stated, not implied.

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