score_architecture

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: deterministic behavior ('Same consultation always produces same results'), prerequisites ('Requires pattern_assessment steps to have been logged'), and the computational process. However, it doesn't cover aspects like error handling, performance, or output format details, leaving some behavioral gaps.

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 and front-loaded, starting with the core purpose. It uses bullet-like phrasing ('computes: maturity level...') to list outputs efficiently. However, the sentence structure is slightly dense, and some phrasing ('Same consultation always produces same results') could be more streamlined, though all content earns its place.

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 complexity (scoring tool with 3 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose, prerequisites, and outputs but lacks details on the return format (e.g., structure of maturity scores or roadmap), which is critical since there's no output schema. This leaves gaps for an AI agent to fully understand what to expect.

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 schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'consultation_id' further or provide examples). This meets the baseline of 3, as the schema does the heavy lifting, but the description doesn't compensate with extra insights.

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: 'Deterministic architecture scoring from stored pattern assessments' and details what it computes (maturity level, pattern status, gap analysis, etc.). It distinguishes itself from siblings by focusing on scoring based on logged pattern assessments, though it doesn't explicitly name alternatives. The description is specific about the verb ('computes') and resource ('pattern assessments'), but lacks direct sibling differentiation.

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 some usage context: 'Requires pattern_assessment steps to have been logged during step 3 (traverse graph).' This implies when to use it (after logging assessments) but doesn't explicitly state when not to use it or name alternative tools for similar tasks. The guidance is helpful but incomplete, as it doesn't compare to siblings like 'consultation_report' or 'critique_consultation'.

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