brilliant-directories-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as a read operation ('Get') and specifies the return content (fields with labels and required flags), which is adequate for a simple query tool. However, it lacks details on potential side effects, error conditions, or performance considerations (e.g., rate limits), leaving some behavioral aspects unclear. No contradiction with annotations exists since none are provided.

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 highly concise and well-structured, consisting of two sentences that efficiently convey the tool's purpose and usage. The first sentence defines what it does, and the second provides practical guidance. There is no wasted language, and information is front-loaded, making it easy for an AI agent to parse quickly.

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 has no parameters, no annotations, and no output schema, the description is moderately complete. It explains what the tool returns (field definitions with labels and required flags) and its discovery purpose, which is sufficient for basic understanding. However, it doesn't detail the output format (e.g., structure of the returned data) or potential limitations, leaving some contextual gaps that could hinder optimal use by an AI agent.

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 input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't mention any parameters, which is appropriate since none exist. It adds value by clarifying the tool's purpose without redundant parameter details, earning a baseline score of 4 for zero-parameter tools where the description compensates adequately.

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 a specific verb ('Get') and resource ('user field definitions'), explaining it returns available fields with labels and required flags. It distinguishes itself from sibling tools like 'getUser' or 'getUserMeta' by focusing on field definitions rather than user data or metadata. However, it doesn't explicitly contrast with 'getPostFields' or 'getPortfolioGroupFields' which serve similar purposes for other entities.

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 usage context by stating 'Use this to discover custom fields,' indicating it's for discovery purposes. It implies when to use it (to understand available fields) but doesn't explicitly mention when not to use it or name alternatives like 'getUser' for actual user data. Given the sibling tools include many 'get' operations for specific data, this guidance is helpful but could be more precise about differentiation.

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