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Mojave-Studio

PelaPela MCP Server

by Mojave-Studio
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Hybrid

PelaPela

Model response tuning for translation work — naturalness, tone, and script guidance, parameterized by language pair. Ships as a Claude Code skill and as an MCP server, so the same tuning methodology works in Claude, ChatGPT (via MCP), and any other MCP-compatible host.

This tunes how a model phrases a translation — word choice, register, script — not the translation engine itself. Point it at whatever model you're already calling.

Why

LLM translations often read as "translated" rather than natural: literal transliteration of ordinary words, mismatched formality, wrong script for the target audience. This package encodes a small, explicit set of rules for catching that, plus a cheap self-check step (fix it yourself before returning, rather than burning a second model call).

Related MCP server: Translator Pro Ai

Use in Claude Code

Install the plugin, then invoke per language pair with optional tone/script args, in any order:

/pela:en-jp friendly kanaonly
/pela:en-es formal

Supported today: en-jp, en-es. See Adding a pair to extend it.

Use via MCP (ChatGPT and other hosts)

ChatGPT's old Plugins system is deprecated; this ships as an MCP server instead, which ChatGPT and most modern agent hosts support.

npx pelapela-mcp

Or point your MCP host's config at it directly:

{
  "mcpServers": {
    "pelapela": { "command": "npx", "args": ["pelapela-mcp"] }
  }
}

It exposes one prompt and one tool, both named pela_translate_tuning, taking pair (required), tone (optional, default neutral), and script (optional, where the pair supports one).

Hosted / remote MCP (ChatGPT Apps submission, other remote-only hosts)

npx pelapela-mcp above runs over stdio — fine for local hosts (Claude Desktop, most CLI agents), but ChatGPT's app directory and some other hosts require a public HTTPS /mcp endpoint instead. worker/ has that variant, built on Cloudflare Workers + the Agents SDK (McpAgent), same pela_translate_tuning tool, same core/buildTuning.mjs source of truth — just a different transport.

Live: https://pelapela-mcp.jess-901.workers.dev/mcp

cd worker
npm install
npm run dev      # local HTTP test at http://localhost:8788/mcp
npm run deploy   # publish to your own Cloudflare account

Submitting to ChatGPT's app/plugin directory beyond hosting it is a manual, identity-verified process through OpenAI's own submission portal — not something this repo can automate.

Adding a pair

Everything lives in core/buildTuning.mjs — a single PAIR_NOTES entry per pair (naturalness rules, tone notes, script variants where applicable). Add an entry there, add a matching skills/pela-<pair>/SKILL.md following the existing two as a template, and both the Claude skill and the MCP server pick it up automatically (the MCP server reads PAIRS directly from the same module — nothing to duplicate).

Development

npm install
npm test                    # unit tests for the instruction builder
node tests/mcp-smoke.mjs    # real MCP client/server round-trip check

License

MIT

Available Tools

1 tool
pela_translate_tuningPela translation tuningA
Read-only

Return translation tuning instructions (naturalness, tone, script) for a given language pair.

ParametersJSON Schema
NameRequiredDescriptionDefault
pairYesLanguage pair. One of: en-jp, en-es.
toneNoTone. One of: neutral, friendly, formal, casual. Defaults to neutral.
scriptNoScript mode, where the pair supports one. One of: default, kanaonly, kanjionly, romaji. Defaults to the pair's standard orthography.

Output Schema

ParametersJSON Schema
NameRequiredDescription
pairYes
toneYes
scriptYes
instructionsYes

TDQS

A4.3/5.0
Behavior4/5

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

The readOnlyHint annotation already indicates a safe read operation. The description adds useful context beyond this by specifying that the instructions pertain to naturalness, tone, and script, which is not captured in the annotation. There is no contradiction.

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, front-loaded sentence with no unnecessary words. It immediately states the action and the outcome, making it highly concise and well-structured.

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?

Given the presence of an output schema, the description does not need to explain return values. The schema fully documents parameters, annotations cover the safety profile, and the tool is simple enough that the description, schema, and annotations together provide comprehensive context.

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?

The input schema provides 100% coverage with detailed descriptions and enums for all three parameters. The tool description adds minimal parameter-related meaning beyond what the schema already contains, so it does not significantly compensate for any gaps.

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 uses the specific verb 'Return' and clearly identifies the resource as 'translation tuning instructions' scoped to 'a given language pair'. This unambiguously conveys the tool's purpose and differentiates it from translation execution tools.

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 clearly states the purpose and implies usage when tuning instructions are needed. However, it does not explicitly mention when not to use the tool, and no alternatives are noted since there are no sibling tools.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedpela_translate_tuning
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TDQS

A4.3/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusing it with another tool. The tool's purpose is clearly described, and there are no overlapping tools to cause misselection.

Naming Consistency5/5

The single tool name follows a clear descriptive pattern (prefix + action + object). There is no inconsistency because there is only one tool, so the naming is trivially consistent.

Tool Count3/5

The server contains exactly one tool, which feels thin for an MCP server but is borderline appropriate given the narrow scope of returning translation tuning instructions. A count of 1 is at the lower end of the acceptable range.

Completeness4/5

For the stated purpose of providing translation tuning instructions, the tool covers the core need. However, there is no way to discover supported language pairs or obtain batch instructions, which is a minor gap that agents can work around with external knowledge.

Maintenance

ActivitySlowing
ResponsivenessNo issues

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