brandsystem-mcp

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

With no annotations provided, the description carries full burden for behavioral disclosure. It does well by describing the analysis outputs (voice fingerprint metrics, vocabulary frequency, claims quality, etc.), the file creation behavior ('Writes .brand/messaging-audit.md'), and the return format ('Returns structured analysis with scores'). However, it doesn't mention potential limitations like processing time, error conditions, or authentication requirements, which would be helpful for a tool with no 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 appropriately sized and front-loaded, starting with the core purpose. Every sentence adds value: the first establishes context, the second provides usage triggers, the third details analysis components, and the fourth explains sequencing and output. While efficient, it could be slightly more concise by combining some of the analysis detail into a more compact list format.

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 (analyzing multiple linguistic dimensions) and lack of annotations/output schema, the description does well to explain what the tool analyzes, what it produces, and how it fits into a workflow. It covers the behavioral aspects adequately though could benefit from mentioning any limitations or prerequisites. The absence of an output schema means the description must explain returns, which it does with 'Returns structured analysis with scores'.

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 both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions analyzing 'website' and 'pages' generally but provides no additional syntax, format, or constraint details. 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 ('audit', 'analyzes', 'writes') and resources ('brand currently sounds on its website', 'voice fingerprint', 'vocabulary frequency', etc.). It explicitly distinguishes this from sibling tools by mentioning it's 'the first step in Session 3' and that 'After this, run brand_compile_messaging to define how the brand *should* sound', creating clear 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 explicit usage guidance with multiple trigger phrases ('analyze my voice', 'brand voice audit', 'how does my brand sound?', 'start Session 3') and clear sequencing instructions ('first step in Session 3', 'After this, run brand_compile_messaging'). It specifies both when to use this tool and what should follow it, offering complete guidance for the agent.

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