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governed_sample

Read-only

Request a governed LLM completion that is classified, policy-checked, optionally gated, and recorded in a forensic ledger for compliance.

Instructions

Request a governed LLM completion via MCP Sampling. The client performs the model call — the server governs when, how, and under what constraints sampling is allowed. Every request is classified, policy-checked, optionally gated, and recorded in the forensic ledger.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
purposeYesWhy this sampling is happening. Determines MAI classification. gate_review_assist triggers MANDATORY gate.
promptYesThe prompt / question to send to the model
system_promptNoOptional system prompt for the sampling request
contextNoAdditional context prepended to the prompt
max_tokensNoMaximum tokens for the response
include_contextNoContext inclusion mode. "none" (default) minimizes exposure. "thisServer" includes this MCP server context.none
domainNoDomain context for MAI classificationgeneral
agent_nameNoAgent requesting the sample
Behavior1/5

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

The description states that every request is 'recorded in the forensic ledger' (a write operation), contradicting the annotation readOnlyHint=true which declares the tool as read-only. This is a serious inconsistency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loaded with the core purpose, and every sentence adds value about the governance process without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 8-parameter schema and no output schema, the description adequately explains the high-level workflow and governance constraints, though it omits specifics about return values or error handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already describes all parameters. The description adds no parameter-specific information beyond the schema, earning the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: requesting a governed LLM completion via MCP Sampling, distinguishing it from sibling tools by emphasizing the governance and client-mediated nature.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (for governed sampling) but does not provide explicit when-not-to-use or alternatives; however, no alternative sampling tool exists among siblings, making the guidance adequate.

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

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