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Poly-Glot AI Workspace

Poly-Glot AI Workspace

VerifiedFeaturedRemote
hmoses.github.ioAI & Agents0Updated 9w ago

About Poly-Glot AI Workspace

Poly-Glot AI Workspace is a hosted MCP service for multilingual writing and AI workflows, with prompt templates, text processing, and model comparison. It supports BYOM and provides an embedded GUI for browsing templates and editing prompts.

Key features

  1. 01Search and use 1,022 prompt templates
  2. 02Translate and localize across 38 languages
  3. 03Prepare prompt comparisons for up to 8 built-in AI providers
  4. 04Connect custom model endpoints
  5. 05Transcribe audio and detect languages
  6. 06Use an embedded interactive workspace

Use cases

  1. 01Find and fill prompt templates by goal
  2. 02Compare responses to one prompt across AI providers
  3. 03Translate or localize text for a target language
  4. 04Run prompts against a custom model

MCP support

This hosted remote MCP service uses Streamable HTTP and provides 15 tools for templates, prompt building, comparison preparation, BYOM, and language processing. Compatible MCP clients can connect to its endpoint and use the embedded interface in supported hosts.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://br-steep-leaf-ae2o29qz-mcp.compute.c-2.us-east-2.aws.neon.tech/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

15
ParameterTypeRequiredDescription
uiLanguagestringNo

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.