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. It effectively describes key behaviors: batch processing ('multiple content items'), scoring mechanism ('scores each item against brand identity'), statistical computation ('computes corpus-level statistics'), pattern identification ('identifies recurring patterns'), and output generation ('writes a detailed drift report to .brand/drift-report.md'). It doesn't mention permissions, rate limits, or error handling, but covers core operational traits well.

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: the first sentence comprehensively states the tool's purpose and key actions, followed by a specific usage guideline. Every sentence earns its place with no redundant information, making it efficient and well-structured for quick comprehension.

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 (batch auditing with statistical analysis) and no annotations or output schema, the description provides strong contextual completeness. It explains what the tool does, when to use it, and key behaviors like output file generation. However, it doesn't detail the report format or error scenarios, leaving minor gaps for a tool with no structured output documentation.

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 adds no specific parameter semantics beyond what the schema provides (e.g., it doesn't explain 'items' format beyond JSON array or 'threshold' interpretation). Baseline 3 is appropriate when the schema does the heavy lifting, though the description could have enhanced understanding of parameter usage in context.

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 ('batch audit', 'detect systematic brand drift', 'scores each item', 'computes corpus-level statistics', 'identifies recurring patterns', 'writes a detailed drift report') and resources ('multiple content items', 'brand identity', 'corpus-level statistics', 'drift report'). It distinguishes from siblings like 'brand_audit' and 'brand_audit_content' by emphasizing batch processing and systematic drift detection across multiple items.

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 explicitly provides usage guidelines with 'Use when reviewing a content corpus, auditing a website, or checking whether brand identity is being applied consistently across multiple pieces.' This gives clear context for when to invoke this tool versus alternatives, such as single-item audit tools like 'brand_audit' or 'brand_audit_content'.

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