correct_transcription

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

No annotations are provided, so the description carries the burden of disclosing behavior. It explains that the tool may either request the client LLM via Sampling or return a correction prompt for the session LLM to execute. This goes beyond the schema and reveals a non-obvious workflow, though it does not mention potential errors or side effects.

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 organized with a purpose statement, behavioral notes, and an Args section. It is slightly repetitive with phrases like '当前会话的 LLM' and '纠错', but no sentence is wasted. The structure makes it easy to parse.

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 that no annotations are present and an output schema exists, the description covers the full decision flow: pre-call user confirmation, mode selection, Sampling support behavior, and reference_text requirements. This is sufficient for an agent to select and invoke the tool correctly, with no major 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 fully compensate. The Args section defines all three parameters: transcription_text, correction_mode with the exact mode strings and defaults, and reference_text with its requirement and use case. This is strong added value beyond the bare schema.

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 a specific action: invoking the current session's LLM to correct transcription text. It differentiates from the sibling tool transcribe_audio by focusing on correction rather than transcription. It also outlines the two correction modes, further clarifying the tool's purpose.

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 explicitly instructs to ask the user before calling and to determine which mode to use. It also explains the workflow for MCP clients that support Sampling versus those that do not. It does not explicitly state when not to use the tool, but the guidance is clear enough for most invocation decisions.

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