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 of behavioral disclosure. It mentions the tool returns analysis results but doesn't describe what those results look like, potential side effects, permissions needed, or performance characteristics. The description adds minimal behavioral context beyond the basic operation.

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 with three sentences: purpose statement, return value explanation, and parameter documentation. It's front-loaded with the core functionality and avoids unnecessary elaboration, though the structure could be slightly more polished.

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 an output schema (which handles return values), one parameter with basic documentation in the description, and no annotations, the description is minimally adequate. However, for a tool performing complex textual analysis, it lacks details about input validation, error conditions, or example usage that would help an agent use it 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?

Schema description coverage is 0%, but the description includes an 'Args' section that documents the single parameter 'segment_id' as the ID of the segment to analyze. This adds meaningful semantics beyond the bare schema, though it doesn't explain format constraints or provide examples. With only one parameter, the baseline is appropriately met.

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 analyzes textual ambiguity to distinguish between total indeterminacy and directed subdetermination, specifying it returns what the text closes vs. leaves open and detects asymmetric relations. This is a specific verb+resource combination, though it doesn't explicitly differentiate from sibling tools like 'detect_weak_quantifiers' or 'validate_claim' which might overlap in semantic analysis.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the purpose alone without explicit comparison to sibling tools.

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