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TechQuanta

Prompt Refiner MCP

by TechQuanta

validate_brief

Validate a prompt brief for completeness and receive a score that reveals gaps in intent, structure, or constraints before sending it to an LLM.

Instructions

Validate a prompt brief and report its completeness score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It only states that it validates and reports a score, but gives no details about the validation criteria, whether the operation is read-only, potential side effects, or the nature of the output beyond a score. This is a significant gap for a tool that processes arbitrary object input.

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

Conciseness4/5

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

The description is a single, compact sentence that is easy to parse and front-loads the core purpose. There is no extraneous wording, and it is appropriately sized for a simple tool, though it sacrifices depth for brevity.

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

Completeness2/5

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

Given the tool has a nested object parameter and an output schema, the description is too sparse to be complete. It does not explain the semantics of a prompt brief, the meaning of the completeness score, or how the output schema relates to the validation. An agent would need additional context to use it confidently.

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

Parameters1/5

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

The schema defines a single parameter 'brief' as an object with additionalProperties true, but provides no description. The tool description does not clarify what a 'prompt brief' is, what structure is expected, or what properties are relevant. With 0% schema description coverage, the description fails to compensate, leaving the agent with no guidance on how to construct valid input.

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

Purpose4/5

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

The description clearly states the action (validate a prompt brief) and the result (report a completeness score). It is specific about the verb and resource, and although it does not explicitly differentiate from sibling tools, the purpose is distinct from get_schema (retrieving schema) and build_prompt_variants (generating variants), so an agent can infer its role.

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

Usage Guidelines2/5

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 the siblings or in what workflow context it should be invoked. There is no mention of prerequisites, when validation is appropriate, or what happens if the brief is incomplete. The agent is left to infer usage from the purpose.

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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