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

Without annotations, the description carries the burden. It discloses that scores measure 'evidence specificity, not truth', and that public-record evidence for incumbent_vendor is automatically attached. It also mentions the output includes gap-closing questions. However, it does not mention error handling, rate limits, or behavior when required fields are missing beyond schema validation.

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 a single dense paragraph that packs a lot of information. While it is front-loaded with the core purpose, it could be broken into shorter sentences or bullet points for easier parsing. Some phrases are verbose (e.g., 'public-record evidence pulled automatically').

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 there is no output schema, the description adequately explains the return format: an evidence-scored scorecard with gap-closing questions and public-record evidence. It also explains the scoring philosophy. However, it does not specify the structure of the scorecard or how results are presented.

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% and each parameter has a description, but the tool description adds meaningful context beyond the schema. For example, it explains that 'economic_buyer' is 'who has budget/obligation authority' adapting to public sector. The nested 'deal_facts' object is explained in terms of MEDDPICC dimensions.

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 the tool's purpose: 'pressure-test a federal deal' using MEDDPICC dimensions. It distinguishes itself from sibling tools by specifying the federal/public-sector adaptation and the output format (scorecard with gap-closing questions). No other sibling appears to do the same specialized scoring.

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 explains when to use: 'pass what you know per MEDDPICC dimension (leave unknowns empty) and get back an evidence-scored scorecard'. It implies that this is for qualification, not discovery, but does not explicitly state when to use alternatives like 'generate_discovery_questions' or 'incumbent_lookup'. The context is clear, but explicit exclusions are missing.

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