BigContext 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 the tool's behavior: it detects weak quantifiers, explains why they matter (require statistical evidence), and specifies the return values ('allow', 'require_count', 'block'). However, it lacks details on error handling, performance limits, or edge cases, leaving some behavioral aspects uncovered.

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: it starts with the core purpose, explains the rationale, specifies the return values, and provides usage instructions and parameter details in a structured way. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 moderate complexity (single parameter, no annotations, but with an output schema), the description is complete enough. It covers purpose, usage, behavior, and parameters, and since an output schema exists, it doesn't need to explain return values in depth. This provides adequate context for an AI agent to select and invoke the tool correctly.

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 0%, so the description must compensate. It adds meaningful semantics for the single parameter 'text' by explaining it as 'Text to analyze (typically agent output)', which clarifies usage context beyond the bare schema. Since there are no other parameters, this is sufficient, but it doesn't detail format constraints or examples, keeping it from a perfect score.

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 ('detects weak quantifiers') and resources ('text to analyze'), and distinguishes it from siblings by focusing on statistical evidence requirements for quantifiers like 'frequently', 'typically', 'always', 'never'. It explicitly mentions what the tool returns ('allow', 'require_count', or 'block'), making the purpose distinct and well-defined.

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: it specifies when to use ('on agent output BEFORE returning to user'), the context ('text to analyze, typically agent output'), and implies alternatives by highlighting its unique focus on weak quantifiers, which differentiates it from sibling tools like 'validate_claim' or 'detect_inference_violations'. This gives clear direction on application timing and scope.

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