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 explains what the tool does (detects narrative voice types) and provides workflow guidance, but doesn't disclose performance characteristics, error conditions, rate limits, or authentication requirements. The description adds value by explaining the voice type distinctions and workflow context, but doesn't provide comprehensive behavioral information.

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 efficiently structured with a clear purpose statement, specific voice type distinctions, usage guidance, and parameter explanations. Every sentence adds value, and the information is front-loaded with the most critical details. The 'CRITICAL' label appropriately emphasizes importance without being verbose.

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 (narrative voice classification), no annotations, and the presence of an output schema, the description provides good context about what the tool does and when to use it. It explains the four voice types and workflow sequencing, which is essential for proper use. The output schema will handle return value documentation, so the description appropriately focuses on usage context rather than output details.

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?

With 0% schema description coverage for 2 parameters, the description must compensate. It explains that 'segment_id' identifies the segment to analyze and that 'domain_vocabulary' is optional and 'for enhanced detection.' This provides meaningful context about what each parameter represents, though it doesn't specify format requirements or constraints for the domain_vocabulary object.

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 explicitly states the tool's purpose as detecting narrative voice types in text segments, with specific examples of the four voice types it distinguishes. It clearly differentiates this from sibling tools like 'identify_speaker' or 'detect_divine_agency_without_speech' by focusing specifically on narrative voice classification rather than speaker identification or agency detection.

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 guidance on when to use this tool: 'Use BEFORE extracting "divine actions" to avoid confusing retrospective prayer with primary divine agency.' This gives clear context about its sequencing in a workflow and distinguishes it from related tools that might handle different aspects of text analysis.

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