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promptshift_transpile

Transpile agent instructions, master prompts, and modular rules between Cursor, Claude, OpenAI, and Gemini to migrate AI coding configurations. Runs in memory with AST mapping and secret redaction.

Instructions

Transpile agent instructions, master prompts, and modular rules across Cursor (.cursorrules / .cursor/rules/*.mdc), Anthropic Claude (CLAUDE.md / Claude Projects XML), OpenAI Assistants, and Google Gemini. Use this tool when migrating or porting AI coding configurations between different developer environments. Do not use for general natural language translation or non-AI code refactoring. Operates 100% in-memory with deterministic AST mapping and active secret sanitization.

Input Schema

NameRequiredDescriptionDefault
rulesNoOptional array of modular rule files, each containing a filename and text content.
license_keyNoOptional Polar.sh Order ID or Pro Developer License Key to unlock repositories with >2 modular rule files.
system_promptYesThe master instruction prompt or primary rule document content to transpile.
source_platformYesOriginating AI coding assistant or rule format (e.g., cursor, claude, gemini).
target_platformYesDestination AI ecosystem format to transpile into.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses important traits: operates 100% in-memory, uses deterministic AST mapping, and performs active secret sanitization. It does not describe output shape or error behavior, but the key operational model is stated.

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 three focused sentences. It front-loads the core action and supported platforms, then adds usage boundaries and behavioral context with no wasted text.

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?

The description covers purpose, usage boundaries, and key behavioral traits for a 5-parameter tool with full schema coverage. The main remaining gap is that, with no output schema, it does not explain the return format or result structure.

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 all five parameters are already documented in the input schema. The description does not add meaning beyond the schema for source_platform, target_platform, system_prompt, rules, or license_key, so the baseline of 3 is appropriate.

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 gives a specific verb (Transpile) and resource (agent instructions, master prompts, modular rules) and names supported source/target formats. It clearly distinguishes the tool from general translation or non-AI refactoring, but it does not explicitly differentiate from sibling tools promptshift_sanitize_secrets and promptshift_validate_ast.

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?

It states when to use the tool (migrating or porting AI coding configurations between environments) and when not to use it (general natural language translation or non-AI code refactoring). It does not name alternative sibling tools or conditions for using them instead.

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