get_model_info

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

With no annotations, the description carries the full burden and does well: it discloses the exact returned quantities (type, feature count, training size, AUC-ROC, precision/recall, recommended threshold) and flags an 'honest limitations note' plus an educational-not-lending-advice caveat. It does not explicitly state that the call is read-only/auth-free, which is the only meaningful gap.

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?

Purpose and usage guidance are front-loaded in the first two sentences, followed by a single enumeration of outputs. The output enumeration is somewhat redundant given an output schema exists, but it is compact and earns its place by adding interpretation context.

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 zero-parameter, annotated-free read tool with an output schema, the description covers purpose, usage, and expected return content. The only under-covered aspect is the explicit absence of side effects or auth requirements, which is minor for an information-lookup 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 for a parameterless tool applies, and the description correctly adds no misleading parameter detail.

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 ('Describe the model itself') and immediately scopes it away from borrower-specific siblings with 'no borrower needed.' An agent can distinguish this from score_borrower, explain_prediction, and compare_borrowers without opening any 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 a clear trigger — 'questions about how good the model is or how it should be used' — and an implicit exclusion ('no borrower needed') that separates it from borrower-focused siblings. It stops short of naming those sibling tools as alternatives, which keeps it from a full 5.

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