BigContext MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses the return format ('speaker name, confidence level, and evidence') and scope ('domain-agnostic'), which adds useful context. However, it lacks details on behavioral traits like error handling, performance characteristics (e.g., speed, accuracy), or side effects (e.g., whether it modifies data). The description is adequate but not rich in behavioral disclosure.

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 appropriately sized. It starts with the core purpose, then covers returns, domain scope, parameters, and output in logical order. Every sentence adds value, with no redundant information. It could be slightly more front-loaded by moving the 'Returns' statement earlier, but overall it's efficient and clear.

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 (4 parameters, 1 required), no annotations, and an output schema present, the description is reasonably complete. It explains the purpose, parameters, returns, and scope. The output schema means the description doesn't need to detail return values, and it covers the essential context. However, it lacks error cases or examples, leaving minor gaps.

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 adds meaningful semantics for all parameters: segment_id ('ID of the segment to analyze'), priority_patterns ('Speaker names to prioritize'), exclude_patterns ('Speaker patterns to flag as ambiguous'), and expected_speaker ('verify this specific speaker'). This clarifies each parameter's role beyond the bare schema, though it doesn't provide format examples or constraints (e.g., pattern syntax).

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: 'Identify who is speaking in a text segment.' It specifies the verb ('identify') and resource ('speaker in a text segment'), making the function unambiguous. However, it doesn't explicitly differentiate this tool from its many siblings (e.g., detect_narrative_voice, detect_performatives), which could have overlapping functionality, preventing a perfect score.

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 mentions being 'domain-agnostic' but doesn't specify contexts or prerequisites, nor does it compare to sibling tools like detect_narrative_voice. Without explicit when/when-not instructions or named alternatives, the agent lacks usage direction beyond the basic purpose.

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