Local Model Suitability MCP

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It goes beyond a simple summary by explaining the return format (LOCAL/CLOUD/EITHER verdict with recommended model), the side effect of a LOCAL verdict (skip the cloud call entirely), and the special behavior of the data_sensitivity parameter (CONFIDENTIAL forces LOCAL). This is rich, actionable context that fully informs the agent of the tool's runtime behavior.

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 appropriately sized for the tool's complexity. Every sentence adds value: it states the purpose, the optimal calling time, the return type, the cost-saving action, and the force mechanism. The only minor redundancy is the two sentences about calling 'BEFORE' routing, but this emphasis is arguably intentional to prevent misuse. It is well-structured and front-loaded.

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?

Given the tool has no sibling tools and a comprehensive output schema, the description covers all essential aspects: what it does, when to call it, what it returns, how to force LOCAL, and the general rule to always check. The output schema handles return structure, while the description supplies the surrounding context. Nothing is left ambiguous.

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 description coverage is 100%, so the baseline is 3. The description does not add meaningful semantic detail beyond the schema: the data_sensitivity behavior (CONFIDENTIAL forces LOCAL) is already fully described in the schema, and the task and quality_threshold parameters are likewise already well-documented. No additional guidance is needed, but also none is provided, so it stays at baseline.

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 opens with a clear, specific action: 'Checks whether a task can be handled by a local model instead of a cloud API.' This distinguishes it from the alternative (cloud routing) and states the tool's core function unambiguously. It also names the exact output ('LOCAL/CLOUD/EITHER verdict with recommended model'), leaving no doubt about its purpose.

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 gives explicit, imperative guidance: 'Call this BEFORE your agent routes any task to a cloud model' and 'Do not route to cloud without checking local viability first.' It also specifies the precise moment to invoke the tool ('at the moment a task is ready to be sent and the model has not yet been selected') and the rationale (cost savings). This leaves no ambiguity about when to use it.

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