@three-ws/ibm-watsonx-mcp

@three-ws/ibm-watsonx-mcp

MCP server for IBM watsonx.ai by three.ws — chat, text generation, embeddings, tokenization, and model discovery with IBM Granite foundation models. Uses your own IBM Cloud credentials.

npx -y @three-ws/ibm-watsonx-mcp
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MCP server for IBM watsonx.ai — chat, generation, embeddings, tokenization, forecasting, and model discovery, on your own IBM Cloud account.

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Install · Quick start · Tools · Requirements · three.ws


A Model Context Protocol server that exposes IBM watsonx.ai — IBM Granite foundation models and anything else in your account — to MCP clients such as Claude Desktop, Claude Code, and Cursor. It talks directly to the watsonx.ai as-a-Service REST API with your own IBM Cloud credentials: it mints an IAM bearer token from your API key, caches it until just before expiry, and scopes every call to your project. No intermediary backend, no telemetry, no mock data.

Community-built and not affiliated with IBM. Registry name: io.github.nirholas/ibm-watsonx.

#Install

npm install @three-ws/ibm-watsonx-mcp

Run it directly with npx (no install needed):

WATSONX_API_KEY=... WATSONX_PROJECT_ID=... npx @three-ws/ibm-watsonx-mcp

Or install globally to get the ibm-watsonx-mcp binary on your PATH:

npm install -g @three-ws/ibm-watsonx-mcp

#Quick start

With Claude Code, one command:

claude mcp add ibm-watsonx -e WATSONX_API_KEY=... -e WATSONX_PROJECT_ID=... -- npx -y @three-ws/ibm-watsonx-mcp

Or add the server to your MCP client config (claude_desktop_config.json, Cursor's mcp.json):

{
	"mcpServers": {
		"ibm-watsonx": {
			"command": "npx",
			"args": ["-y", "@three-ws/ibm-watsonx-mcp"],
			"env": {
				"WATSONX_API_KEY": "your-ibm-cloud-api-key",
				"WATSONX_PROJECT_ID": "your-watsonx-project-id"
			}
		}
	}
}

Restart the client and the six tools below appear. Inspect the surface manually with the MCP Inspector:

npx -y @modelcontextprotocol/inspector npx @three-ws/ibm-watsonx-mcp

#Tools

Tool What it does
watsonx_chat Chat completion from a list of role/content messages. Returns the reply plus token usage.
watsonx_generate Raw prompt completion with decoding control (greedy/sample, stop sequences).
watsonx_embed Embedding vectors for one or more texts (up to 1000). One vector per input plus dimensionality.
watsonx_tokenize Token count (and optionally the tokens) for a text against a model tokenizer.
watsonx_forecast Zero-shot time-series forecasting with an IBM Granite TimeSeries (TinyTimeMixer) model.
watsonx_list_models List the foundation models available to your account and region, optionally filtered.

Every tool is a read-only model-inference call — nothing in your environment or IBM account is modified — and declares MCP tool annotations (readOnlyHint, openWorldHint, idempotentHint) so clients can reason about side effects.

#Input parameters

watsonx_chat — messages (required: array of { role, content }, roles system/user/assistant), model, max_tokens (1–4096), temperature (0–2), top_p (0–1).

watsonx_generate — input (required), model, max_new_tokens (1–4096), temperature (0–2), decoding_method (greedy/sample), stop_sequences.

watsonx_embed — inputs (required: 1–1000 texts), model.

watsonx_tokenize — input (required), model, return_tokens (boolean).

watsonx_forecast — timestamps (required: ISO-8601, uniform cadence, oldest first), values (required: numeric, same length), freq (required: pandas cadence, e.g. 1h, 15min, 1D), model, target_column, prediction_length (1–96).

watsonx_list_models — filter (e.g. function_text_generation, function_embedding), limit (1–200, default 100).

#Example calls

// watsonx_chat
{ "messages": [{ "role": "user", "content": "Explain MCP in one sentence." }] }

// watsonx_generate
{ "input": "Write a haiku about Kubernetes.", "decoding_method": "sample", "temperature": 0.7, "max_new_tokens": 60 }

// watsonx_embed
{ "inputs": ["lakehouse", "data warehouse"] }

// watsonx_forecast
{ "timestamps": ["2025-01-01T00:00:00Z", "2025-01-01T01:00:00Z"], "values": [1200, 1180], "freq": "1h", "prediction_length": 24 }

// watsonx_list_models
{ "filter": "function_embedding" }

#How auth works

WATSONX_API_KEY is exchanged for a short-lived IAM bearer token at https://iam.cloud.ibm.com/identity/token (grant_type=urn:ibm:params:oauth:grant-type:apikey). The token is cached in-process and refreshed about five minutes before it expires. Your API key never leaves your machine except in that single IAM exchange.

#Requirements

#Environment variables

Variable Required Default
WATSONX_API_KEY yes —
WATSONX_PROJECT_ID yes (or WATSONX_SPACE_ID) —
WATSONX_SPACE_ID alternative to WATSONX_PROJECT_ID —
WATSONX_URL no https://us-south.ml.cloud.ibm.com
WATSONX_MODEL_ID no ibm/granite-3-8b-instruct
WATSONX_EMBED_MODEL_ID no ibm/granite-embedding-278m-multilingual
WATSONX_FORECAST_MODEL no ibm/granite-ttm-512-96-r2
WATSONX_API_VERSION no 2024-05-31
WATSONX_TS_API_VERSION no 2025-02-11 (time-series API date)
WATSONX_TIMEOUT_MS no 60000
WATSONX_IAM_URL no https://iam.cloud.ibm.com/identity/token

Regional hosts: us-south, eu-de, eu-gb, jp-tok, au-syd, ca-tor — e.g. https://eu-de.ml.cloud.ibm.com.


Part of the three.ws SDK suite — 3D AI agents, on-chain identity, and agent payments.
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