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 performs a validation check based on distance constraints, returns a result, and implies it's a read-only operation (no destructive effects mentioned). However, it doesn't specify error handling, performance characteristics, or authentication needs, leaving some gaps in transparency.

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: the first sentence states the core purpose, followed by usage guidelines, and then parameter and return details in a structured format. Every sentence earns its place by adding value, with no redundant or vague phrasing.

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 (validation with three parameters), no annotations, and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage, parameters, and behavioral context adequately, leaving no significant gaps for an AI agent to understand 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 fully. It does so by explaining all three parameters: 'base_segment_id' as the anchor, 'target_segment_id' as the referenced segment, and 'max_distance' with semantics (0 = same, 1 = adjacent) and a default value. This adds crucial meaning beyond the bare schema, making parameter usage clear.

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 ('check if two segments are adjacent') and resources ('segments'), distinguishing it from siblings like 'get_adjacent_segments' (which retrieves segments) or 'compare_segments' (which likely compares content). It explicitly mentions 'within allowed distance' and 'enforce constraints,' making the purpose unambiguous.

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 guidance: 'Use to enforce "same verse or verse+1" constraints' and 'Prevents narrative jump violations.' This clearly indicates when to use this tool (for proximity validation in narrative contexts) and implies alternatives are not needed for this specific constraint-checking task, given the sibling tools focus on other validations like 'validate_claim' or 'validate_literal_quote.'

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