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TechQuanta

Prompt Refiner MCP

by TechQuanta

get_schema

Retrieve the JSON schema for a deterministic prompt brief to structure and validate prompt inputs before sending them to an LLM.

Instructions

Return the JSON schema for a deterministic prompt brief.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It clearly indicates a read operation ('Return') and does not imply any side effects. However, it does not explicitly state that it is non-destructive or requires any special context, though for a simple get this is likely sufficient.

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 a single, well-formed sentence with no filler. It is front-loaded with the action and object, making it immediately scannable.

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

Completeness5/5

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

The tool is extremely simple (no parameters, no nested objects, has an output schema). The description fully explains what it does, and the output schema itself documents the return structure. Nothing essential is missing.

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

Parameters4/5

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

The tool has zero parameters, so the schema covers 100% of parameter semantics vacuously. The description adds no parameter information, but none is needed. Per calibration, a baseline of 4 is appropriate for zero-parameter tools.

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 states a clear verb 'Return' and a specific resource 'JSON schema for a deterministic prompt brief'. It is immediately distinct from sibling tools like validate_brief and build_prompt_variants, which focus on validation and generation rather than retrieval of a schema.

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?

No guidance is given on when to use this tool versus the siblings. Since the tool has zero parameters and a narrow purpose, an agent might still benefit from a note that this is for retrieving the schema before validating or building variants, but none is provided.

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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