How do I use Djelia MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Djelia MCP Server Translate 'How are you?' to Bambara" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Djelia MCP Server

Official

by djelia-org

Overview Schema Source Related Servers Discussions Admin

Python

Remote

🎙️ Djelia MCP Server

An MCP server for Djelia — bring Bambara transcription, translation, and text-to-speech to any LLM.

Built with FastMCP v3 · Python 3.11+ · uv-managed

✨ Overview

Djelia is a linguistic-AI platform focused on African languages — currently Bambara (bam_Latn), with translation bridging to French (fra_Latn) and English (eng_Latn).

This server wraps the Djelia REST API behind the Model Context Protocol, so any MCP-compatible client (Claude Desktop, Cursor, Cline, your own agent) can call Djelia's models as native tools — no SDK glue, no HTTP plumbing in your prompt.

What you get

#	Tool	Direction	V1 / V2	Returns
1	`list_supported_languages`	—	—	JSON list
2	`translate`	text → text	v1	translated text
3	`transcribe`	audio → text	v2	text + segment timing
4	`text_to_speech`	text → audio	v2	audio content block

Design note: V2 APIs are exposed for transcription and TTS because they supersede V1 (richer voices via description, format control). True /stream endpoints are omitted — MCP is request/response, so we aggregate the stream inside the tool. Add raw streaming tools only if a use case needs them.

Related MCP server: sarvam-tools

🏗️ Architecture

flowchart LR
    subgraph Client["MCP Client"]
        LLM["LLM / Agent<br/>(Claude, Cursor, …)"]
    end

    subgraph Server["djelia-mcp-server (this repo)"]
        MCP["FastMCP Server<br/><i>4 tools, stdio · sse · http</i>"]
        HANDLERS["Tool Handlers<br/>translate · transcribe · tts"]
        HTTP["httpx.AsyncClient<br/><i>x-api-key header</i>"]
        MCP --> HANDLERS --> HTTP
    end

    subgraph Djelia["Djelia Cloud API"]
        T1["/v1/translate"]
        T2["/v2/transcribe"]
        T3["/v2/tts"]
    end

    LLM -- "MCP JSON-RPC" --> MCP
    HTTP -- "HTTPS" --> T1
    HTTP -- "HTTPS" --> T2
    HTTP -- "HTTPS" --> T3

Key design choices

One shared HTTP client — x-api-key header injected once per request; key read from DJELIA_API_KEY env var.
base64 for audio input — MCP payloads are JSON; audio bytes travel as base64 so it works across any client. A magic-byte sniffer (_guess_ext) recovers the right file extension for the multipart upload.
Audio output as a content block — FastMCP's Audio helper returns a proper MCP audio block (clients receive it base64-encoded).

🔧 How each tool works

1 · `list_supported_languages`

Returns the language codes you'll pass to translate.

sequenceDiagram
    participant C as Client
    participant S as MCP Server
    participant D as Djelia API
    C->>S: list_supported_languages()
    S->>D: GET /api/v1/models/translate/supported-languages
    D-->>S: [{code, name}, ...]
    S-->>C: structured list

2 · `translate`

sequenceDiagram
    participant C as Client
    participant S as MCP Server
    participant D as Djelia API
    C->>S: translate(source, target, text)
    S->>D: POST /api/v1/models/translate (JSON)
    D-->>S: { "text": "<translated>" }
    S-->>C: structured dict

Parameters

Name	Type	Values
`source`	enum	`bam_Latn` · `fra_Latn` · `eng_Latn`
`target`	enum	`bam_Latn` · `fra_Latn` · `eng_Latn`
`text`	string	the text to translate

3 · `transcribe` (Bambara audio → text)

The tool decodes base64 → sniffs the format → uploads as multipart to the V2 transcription endpoint.

sequenceDiagram
    participant C as Client
    participant S as MCP Server
    participant D as Djelia API
    C->>S: transcribe(audio_base64)
    S->>S: base64decode + guess_ext (mp3/wav/m4a/ogg)
    S->>D: POST /api/v2/models/transcribe (multipart)
    alt single text response
        D-->>S: { "text": "..." }
    else segmented response
        D-->>S: [{ text, start, end }, ...]
    end
    S-->>C: ToolResult (structured + text)

4 · `text_to_speech` (text → Bambara audio)

sequenceDiagram
    participant C as Client
    participant S as MCP Server
    participant D as Djelia API
    C->>S: text_to_speech(text, description, format)
    S->>D: POST /api/v2/models/tts (JSON)
    D-->>S: binary audio bytes
    S-->>C: Audio content block (base64)

Parameters

Name	Type	Values
`text`	string	text to synthesize
`description`	string	voice style, e.g. `"calm male voice, slow pace"`
`format`	enum	`mp3` (default) · `wav` · `wav_8k` · `ulaw_8k`

🚀 Quickstart

1 · Prerequisites

uv installed
A Djelia API key — get one at https://console.djelia.cloud

2 · Install dependencies

git clone <your-repo-url> djelia-mcp-server
cd djelia-mcp-server
uv sync

3 · Set your API key

cp .env.example .env
# edit .env:
#   DJELIA_API_KEY=your_key_here

The server reads DJELIA_API_KEY from the environment. It fails fast with a clear message if the key is missing.

🌐 Transports

FastMCP supports three transports. Pick the one your client expects.

flowchart TB
    subgraph "Transport decision"
        STDIO["stdio<br/><b>default</b><br/>Claude Desktop, CLI agents"]
        SSE["sse<br/><b>legacy</b><br/>older MCP clients"]
        HTTP["http / streamable-http<br/><b>recommended for network</b>"]
    end
    STDIO -. "stdin/stdout" .-> Srv["FastMCP Server"]
    SSE   -. "HTTP + EventSource<br/>GET /sse/" .-> Srv
    HTTP  -. "HTTP POST<br/>POST /mcp/" .-> Srv

Mode	Command	Endpoint
stdio (default)	`uv run fastmcp run server.py`	—
sse (legacy)	`uv run fastmcp run server.py -t sse -p 8000`	`http://127.0.0.1:8000/sse/`
http	`uv run fastmcp run server.py -t http -p 8000`	`http://127.0.0.1:8000/mcp/`
streamable-http	`uv run fastmcp run server.py -t streamable-http -p 8000`	`http://127.0.0.1:8000/mcp/`

Override host/port with --host / -p. See all options: uv run fastmcp run --help.

Direct Python (without the `fastmcp` CLI)

Transport is read from DJELIA_TRANSPORT (stdio | sse | http):

DJELIA_TRANSPORT=sse DJELIA_HOST=127.0.0.1 DJELIA_PORT=8000 uv run python server.py

🤝 Client configuration

Claude Desktop / Cursor (stdio)

Drop this into your MCP client config:

{
  "mcpServers": {
    "djelia": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/djelia-mcp-server",
        "fastmcp",
        "run",
        "server.py"
      ],
      "env": {
        "DJELIA_API_KEY": "your_api_key"
      }
    }
  }
}

Remote / networked client (SSE or HTTP)

Run the server with -t sse or -t http, then point your client at the endpoint (e.g. http://your-host:8000/mcp/).

🗂️ Project layout

djelia-mcp-server/
├── server.py         # all 4 tools + httpx client + transport switch
├── pyproject.toml    # uv project (fastmcp + httpx)
├── .env.example      # DJELIA_API_KEY template
├── .gitignore
└── README.md

One file of code — by design. Tools are co-located because they share one client and one concern (calling Djelia).

🧪 Verifying it works

Smoke-test that all tools register and the server boots on every transport:

# list registered tools
uv run python -c "import asyncio, server; \
  [print(' -', t.name) for t in asyncio.run(server.mcp.list_tools())]"

# boot a transport
uv run fastmcp run server.py -t sse -p 8000

You should see 4 tools listed, and the FastMCP banner with transport 'sse' followed by Uvicorn running.

📚 References

Djelia API docs — https://djelia.cloud/redoc
Djelia console (get an API key) — https://console.djelia.cloud
FastMCP — https://gofastmcp.com
Model Context Protocol — https://modelcontextprotocol.io

📝 License

MIT

Available Tools

4 tools

list_supported_languagesA

List languages supported by Djelia translation.

Returns a list of {"code": str, "name": str}. Codes: bam_Latn (Bambara), fra_Latn (French), eng_Latn (English).

Inputs: no named parameters. Returns: result

text_to_speechA

Synthesize Bambara speech from text with desired voice description.

Inputs: text (required), description (required), format.

transcribeB

Transcribe Bambara speech to text using Djelia V2.

Inputs: audio_base64 (required).

translateA

Translate text from source to target language.

Use list_supported_languages to get valid codes. Returns {"text": ""}.

Inputs: text (required), source (required), target (required). Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored 2026-08-02 04:21 across 4 tools

Disambiguation5/5

Each tool targets a distinct operation: listing languages, translating text, transcribing speech, and synthesizing speech. There is no overlap or ambiguity between them.

Naming Consistency3/5

Tool names mix verb-led formats like 'list_supported_languages' and 'translate' with the noun phrase 'text_to_speech'. The pattern is not fully consistent but remains readable and predictable.

Tool Count5/5

Four tools is a well-scoped size for a language services server, covering translation, transcription, TTS, and language discovery without bloat or thinness.

Completeness4/5

The tool surface covers the core language lifecycle: discover languages, translate, transcribe, and synthesize. Minor gaps exist (e.g., no explicit voice listing or language detection), but they are not obvious dead ends for the stated purpose.

Maintenance

ActivityInactive

ResponsivenessLimited data

Resources

Related MCP Connectors

Kooma — Bambara AIA
ai.kooma
Bambara AI over MCP: text-to-speech, transcription and translation (Bamanankan + more).
Speech Processing Text-to-Speech Language Translation
31
https://mcp.kooma.ai/mcp
mcp-aichat
io.github.AceDataCloud
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Speechtotext Mcp
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MCP server for Speech-to-Text
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https://speechtotext_mcp.mcpize.run/mcp
RiverScriptA
com.riverscript
MCP server for RiverScript, an AI transcription platform - fetches transcripts shared via a link.
Audio Processing AI & Machine Learning
11
https://riverscript.com/mcp

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