get_model_meta

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

With no annotations, the description carries the full burden and does add real behavioral facts: raw evaluation metrics are deliberately excluded (a scope limit an agent would otherwise guess wrong about), and the tool is available on every plan (an entitlement constraint). It does not state pagination or shape of the changelog, but for a read-only getter this is solid disclosure.

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?

One sentence, front-loaded with the verb and resource, followed by the returned fields and two caveats. No filler, no restatement of the tool name.

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?

With no output schema, the description does the work of documenting return content by listing the five manifest fields, and it flags the notable exclusion. It stops short of saying the result is a collection (one entry per model) or what a changelog entry looks like, which is the only remaining gap for a zero-param read tool.

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 tool takes zero parameters, so there is nothing for the description to disambiguate — the baseline of 4 applies. The description correctly implies no inputs are needed by framing the call as a global manifest fetch.

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 and resource ('Get the public model manifest for each RaceHooks predictive model') and then enumerates exactly what the manifest contains: prediction target, model family, calibration tier, training date, changelog. No sibling tool covers model metadata, so an agent can route to it unambiguously.

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?

Usage is only implied: 'public model manifest' plus 'Available on every plan' signals it is a no-cost discovery call, but the description never says when to call it (e.g. before interpreting a prediction output) or names any alternative. For a zero-parameter meta endpoint the intent is fairly self-evident, so 3 rather than 1.

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