artefact-mcp-server

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

The description adds valuable behavioral context beyond annotations: it explains the scoring model structure (14.5 points across three categories), describes the return format (tier classification, breakdown, engagement strategy), and mentions the API key requirement for company_id. Annotations already cover read-only, open-world, and idempotent characteristics, so the description appropriately supplements rather than contradicts them.

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 efficiently structured with clear sections: purpose statement, scoring model explanation, parameter guidance with examples, and return format description. Every sentence adds value, and information is front-loaded with the core purpose stated first.

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's complexity (scoring model with multiple dimensions) and 0% schema coverage, the description provides complete context: it explains the scoring methodology, documents both parameter options with examples, describes the return format in detail, and mentions authentication requirements. The existence of an output schema means the description doesn't need to exhaustively document return values.

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?

With 0% schema description coverage, the description fully compensates by explaining both parameters in detail: it clarifies the exclusive OR relationship ('Provide EITHER...'), specifies that company_id requires HUBSPOT_API_KEY, provides a comprehensive example of company_data JSON structure with 11 example keys and their possible values, and explains the purpose of each parameter.

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 specific action ('Score a prospect'), resource ('against the Artefact 14.5-point ICP model'), and scope (evaluating three fit categories). It distinguishes from sibling tools like 'run_rfm' and 'score_pipeline_health' by focusing on ICP qualification rather than RFM analysis or pipeline metrics.

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 provides clear context for when to use this tool (scoring prospects against an ICP model) and mentions the two alternative input methods. However, it doesn't explicitly state when NOT to use it or compare it directly to sibling tools, which would be needed for a perfect score.

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