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mdma

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get-prompt

Returns a named MDMA prompt (mdma-author, mdma-reviewer, or mdma-fixer). For mdma-author, pass an optional variantId (e.g.

Instructions

Returns a named MDMA prompt (mdma-author, mdma-reviewer, or mdma-fixer). For mdma-author, pass an optional variantId (e.g. "anthropic/sonnet-5.5", "openai/gpt-6-sol") to get the model-optimised variant — use list-prompt-variants to see all available ids.

Input Schema

NameRequiredDescriptionDefault
nameYesPrompt name: mdma-author, mdma-reviewer, or mdma-fixer
variantIdNoModel-specific variant id for mdma-author (e.g. "google/gemini-2.5-pro", "openai/gpt-5"). Falls back to the default variant if omitted or unknown.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.2
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / variantId
      Added value: +{
+  "description": "Model-specific variant id for mdma-author (e.g. \"google/gemini-2.5-pro\", \"openai/gpt-5\"). Falls back to the default variant if omitted or unknown.",
+  "type": "string"
+}
  2. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations exist, so the description carries the full behavioral burden, yet it discloses almost nothing beyond the identity of the resource — no error behavior for an unknown name, no note on caching, auth, or whether the returned prompt is text or a structured object. The fallback-on-unknown-variant behavior is present only in the schema, not the description.

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?

Two tight sentences, front-loaded with the core purpose, then the conditional variant guidance. Every clause earns its place and nothing is padded.

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?

For a simple two-parameter retrieval tool with full schema coverage and no output schema, the description covers purpose, valid inputs, and the sibling needed for variant discovery. Only the shape of the returned prompt is left unstated, which is a minor gap.

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 description coverage is 100%, so both parameters are already documented, including the fallback rule; the description largely restates this with different example variant ids. Baseline 3 is warranted because it adds little beyond the structured field text.

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?

States a specific verb and resource ('Returns a named MDMA prompt') and enumerates the three valid names, so the agent knows exactly what it fetches without opening the schema. It also implicitly distinguishes itself from retrieval siblings like get-doc/get-spec by naming the MDMA prompt domain.

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?

Gives a clear conditional rule: pass variantId only when name is mdma-author, and routes the agent to list-prompt-variants to discover valid ids. There is no explicit when-not/exclusion guidance, but the conditional covers the main decision point.

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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