trigger-sampling-requestTrigger an LLM sampling request to obtain missing required tool arguments, allowing MCP servers to dynamically elicit inputs through the proxy.
Trigger a Request from the Server for LLM Sampling
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt to send to the LLM | |
| maxTokens | No | Maximum number of tokens to generate |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=false (mutation possible), openWorldHint=true (external side effects), and idempotentHint=false (may not be repeatable). The description adds no behavioral detail about side effects, error conditions, or rate limits, leaving the agent underinformed.
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 extremely concise and front-loaded, but it borders on under-specification. It is short and to the point, but lacks substance.
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 side-effect potential (openWorldHint) and lack of output schema, the description is too minimal. It omits details about the response format, error handling, or consequences, making it incomplete for an agent to invoke confidently.
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 has 100% coverage, so parameters are documented, but the description adds nothing beyond that. There is no clarification on the meaning of 'sampling' or how maxTokens is used.
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 states that the tool triggers a request for LLM sampling, which conveys a general action, but it is vague about what resource or context it operates on. It doesn't distinguish it from sibling tools like 'trigger-elicitation-request' or 'trigger-url-elicitation'.
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?
No guidance is provided for when to use this tool versus alternatives. The openWorldHint suggests the tool may operate on external resources, but the description does not explain the use case or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.