brandsystem-mcp

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 and does so effectively. It explains the progressive nature of the tool ('Works progressively: Session 1 scores tokens...'), describes the return format ('Returns 0-100 score with per-dimension breakdown and specific issues'), and clarifies how different input types are handled ('HTML gets visual + voice analysis; plain text gets voice analysis only'). It doesn't mention rate limits or authentication requirements, but provides substantial behavioral context.

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 with the core purpose in the first sentence. Each subsequent sentence adds valuable information about usage, behavior, and exclusions. While slightly dense, there's minimal waste - every sentence serves a clear purpose in helping the agent understand and use the tool correctly.

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 (progressive analysis, multiple analysis types) and lack of annotations or output schema, the description provides substantial context. It explains what the tool does, when to use it, how it behaves progressively, what it returns, and what it doesn't do. The main gap is the lack of output schema details, but the description compensates by describing the return format. For a tool with no annotations, this is quite comprehensive.

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?

The schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add significant parameter semantics beyond what's in the schema descriptions, though it reinforces the progressive nature of the tool that relates to the 'depth' parameter. This meets the baseline expectation when schema coverage is complete.

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 with specific verbs ('check', 'score') and resources ('content', 'text or markup'), and explicitly distinguishes it from sibling tools brand_audit and brand_preflight by stating what it's NOT for. It provides a comprehensive scope covering color/font usage, voice alignment, anti-pattern violations, and message coverage.

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 explicit usage guidelines with multiple examples ('when asked...'), clear context ('after generating any content'), and specific exclusions naming alternative tools ('NOT for .brand/ directory validation (use brand_audit) or HTML/CSS rule checking (use brand_preflight)'). This gives the agent clear direction on when to use this tool versus alternatives.

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