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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable context beyond that: it warns against submitting secrets, personal identifiers, raw private interviews, or confidential evidence, and clarifies the tool is a pre-evidence resolution step, not a readiness verdict. No contradiction with annotations.

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 well-structured with 'Use when' and 'Do not use when' sections and a safety warning. The only minor issue is redundancy: 'Use when' appears twice (first sentence and the following 'Use when:'), making it slightly less concise than ideal. Overall it is efficient 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?

The description provides complete context for an AI agent: when to use (after mvr_first_call), the types of inputs it handles, what it resolves, exclusions, and a data-handling constraint. Since an output schema exists, return value details are not necessary. This is sufficient for both selection and invocation.

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?

Input schema coverage is 100% with each parameter having a description, so the description need not repeat parameter details. It provides context for what the payload is meant to achieve (resolving to country/sector/archetype) but does not add per-parameter semantics beyond what the schema already offers.

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 clearly states that the tool resolves an entity (startup, venture, company, etc.) to a country, sector, and archetype, using the verb 'resolve' and naming the resource. This distinguishes it from siblings like mvr_decision_check, which assesses readiness, and mvr_first_call, which is the preceding step.

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?

Explicit usage guidance is provided: 'Use immediately after mvr_first_call' and specific trigger conditions (user gives company, venture, sector, country, or ambiguous market actor). Do-not-use exclusions are also clear ('Do not use as a readiness verdict or for unrelated entity lookup'), giving an agent unambiguous selection criteria.

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