BigContext 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. It discloses that the tool 'detects parenthetical comments, notes sections, evaluative language,' which adds useful behavioral context beyond basic validation. However, it doesn't cover aspects like error handling, performance implications, or rate limits, leaving some gaps for a mutation-like validation tool.

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 well-structured and front-loaded, starting with the core purpose. Each sentence adds value: the first states the purpose, the second details detection capabilities, the third gives usage guidelines, and the parameter and return sections are clearly separated. There's no wasted text, making it highly efficient.

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 complexity (validation tool with 3 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers purpose, usage, and parameters, and the output schema handles return values, so no need to explain those. However, it could benefit from more detail on behavioral aspects like validation rules or error cases, slightly reducing completeness.

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 0%, so the description must compensate. It lists all three parameters ('output', 'fields', 'allow_commentary') and provides a brief explanation for 'allow_commentary' ('Whether commentary is allowed'). However, it doesn't elaborate on the semantics of 'output' or 'fields' (e.g., format, constraints), resulting in a baseline score of 3 as it adds some but incomplete value.

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: 'Validate that extraction output follows a strict schema.' It specifies the verb 'validate' and the resource 'extraction output,' making it distinct from sibling tools like 'validate_agency_execution' or 'validate_claim.' However, it doesn't explicitly differentiate from all validation siblings, keeping it at 4 rather than 5.

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 context for usage: 'Use when user requests pure data extraction.' This gives a specific scenario for when to apply the tool. However, it doesn't mention when not to use it or name alternatives among the many sibling tools, preventing a score of 5.

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