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MCP server · 11 tools

Our models, as tools for your AI agent

Add one address to Claude, Claude Code, Codex, Cursor or any app that speaks MCP (the Model Context Protocol), and nine Udgam Labs models become tools your agent can call: speech to text, Hindi transliteration, Telugu text, chat, maths, pictures, short video clips and on-device tool calling. Free, no key, no sign-up.

The address

https://mcp.udgamlabs.com/mcp

Streamable HTTP, no sign-in. Opening mcp.udgamlabs.com in a browser shows the same steps.

Add it to your app

Claude (web and desktop app)

  1. Open Customize → Connectors and choose Add custom connector.
  2. Paste the address above as the server URL. It needs no sign-in, so leave the authentication settings empty.
  3. Choose Add, then turn the connector on for a chat from the + menu.

Works on every Claude plan; the Free plan allows one custom connector. On Team and Enterprise plans, an Owner adds it under Organization settings → Connectors.

Claude Code

In a terminal:

claude mcp add --transport http udgamlabs https://mcp.udgamlabs.com/mcp

Codex

In a terminal:

codex mcp add udgamlabs --url https://mcp.udgamlabs.com/mcp

Cursor

Add this to .cursor/mcp.json in your project, or to ~/.cursor/mcp.json for every project:

{
  "mcpServers": {
    "udgamlabs": { "url": "https://mcp.udgamlabs.com/mcp" }
  }
}

VS Code

Run MCP: Add Server from the Command Palette, choose HTTP, paste the address and name it udgamlabs.

Any other MCP app

Add a remote (streamable HTTP) server with the address above. There is no key, and the server keeps no sessions.

The tools

Your agent picks the tool from what you ask. Every result ends with a source line that names the model and links its page, where you can see its numbers and where it falls short.

  • shruti_transcribeShrutiSpeech to text for Hindi, English and Hinglish, from a link to an audio file.Runs on: Free CPU on Hugging Face
  • lekhani_transliterateLekhaniHindi typed in Latin letters to Devanagari (kya haal hai: क्या हाल है), with each word’s runners-up if asked.Runs on: The MCP server itself
  • vemana_continueVemanaContinues Telugu text. A base model: it writes on, it does not answer questions.Runs on: Free CPU on Hugging Face
  • laghu_chatLaghuOne message to a 152M English chat model, with thinking on or off.Runs on: Free CPU on Hugging Face
  • gargi_chatGargiOne message to a 384M English model that thinks step by step on maths problems.Runs on: Free CPU on Hugging Face
  • lilavati_solveLilavatiSolves a maths word problem by writing a short Python program and running it: the answer, how sure it is, and the program.Runs on: Shared GPU on Hugging Face
  • chitra_tiny_generate_imageChitra TinyA 256×256 picture from one sentence, returned as an image your agent can see.Runs on: Shared GPU on Hugging Face
  • drishya_tiny_generate_videoDrishya TinyA 2-second, 256×256 video clip from a sentence, as a link. The weights are for non-commercial use.Runs on: Shared GPU on Hugging Face
  • udgam_384m_tool_callUdgam-384MWhich tool call a 384M on-device model would make for a request, with your own tools. Nothing is run.Runs on: Free CPU on Hugging Face
  • find_small_modelDataMeasured scores of 27 open language models up to 400M parameters, from the Small Model Board.Runs on: The MCP server itself
  • list_udgam_modelsDataEvery Udgam Labs model, with its size, licence, how to run it and where it falls short.Runs on: The MCP server itself

Things to ask

  • Write “kal subah 10 baje milte hain” in Devanagari with Lekhani, and show me the words it was unsure about.
  • Transcribe this recording with Shruti: <a direct link to a wav, mp3, flac or ogg file>
  • Ask Lilavati: a phone costs 15,000 rupees, it is 20% off, then 5% tax is added to the sale price. What is the final price? Check its program.
  • Draw a lighthouse on a rocky coast at sunset with Chitra Tiny, and tell me what you see.
  • Which open language model under 200M parameters scores best? Use the Small Model Board.

Good to know

Speed. Most models run on free shared CPUs on Hugging Face, so a call takes a few seconds, and the first call after a quiet spell waits while the model starts. Lekhani runs on the server itself, so a sentence takes about a second.

Small models make mistakes. Each tool tells your agent what the model is for and where it falls short; check anything that matters.

Your data. What your agent sends goes to the model only to produce the answer. We do not log inputs or answers, and we count calls without IP addresses (privacy). To keep everything on your own device, run the models yourself: each model page shows how.

GPU models. Lilavati, Chitra Tiny and Drishya Tiny run on Hugging Face’s shared GPUs (ZeroGPU), which give callers a small free allowance each day. If it runs out, add your own Hugging Face token as the header X-HF-Token to use your account’s daily GPU minutes. We pass it to Hugging Face for that call only and never store it.

# Claude Code
claude mcp add --transport http udgamlabs https://mcp.udgamlabs.com/mcp --header "X-HF-Token: hf_..."

# Cursor: .cursor/mcp.json
{ "mcpServers": { "udgamlabs": { "url": "https://mcp.udgamlabs.com/mcp", "headers": { "X-HF-Token": "${env:HF_TOKEN}" } } } }

# Codex: ~/.codex/config.toml (the token is read from the HF_TOKEN environment variable)
[mcp_servers.udgamlabs]
url = "https://mcp.udgamlabs.com/mcp"
env_http_headers = { "X-HF-Token" = "HF_TOKEN" }