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@llmgraph/mcp-server

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@llmgraph/mcp-server

A Model Context Protocol (MCP) server that exposes your LLMGraph workflow deployments as MCP tools. Connect it to Claude Desktop, Claude Code, Cursor, or any other MCP client, and your assistant can invoke the workflows you built and deployed on LLMGraph.

Each configured deployment becomes one MCP tool. The server runs over stdio and is designed to be launched with npx, so there is nothing to install permanently.

Prerequisites

  • Node.js 18 or newer

  • A deployed LLMGraph workflow: copy the deployment endpoint URL (shaped like https://llmgraph.ai/api/<graph_id>/<environment>) and an API key from the LLMGraph dashboard

Related MCP server: OpenAPI MCP Server

Configuration

All configuration is via environment variables.

Single deployment (simple path)

Variable

Required

Description

LLMGRAPH_ENDPOINT

yes

Full deployment endpoint URL copied from the dashboard

LLMGRAPH_API_KEY

yes

Secret API key for the deployment

LLMGRAPH_TOOL_NAME

no

Tool name shown to the client (default: invoke_workflow)

LLMGRAPH_TOOL_DESCRIPTION

no

Tool description shown to the model

LLMGRAPH_SCHEMA_MODE

no

input (default) or chat, see below

LLMGRAPH_TIMEOUT_MS

no

Request timeout in milliseconds, positive integer (default: 180000). Applies in both single and multiple deployment modes.

Multiple deployments (advanced path)

Set LLMGRAPH_DEPLOYMENTS to a JSON array; each entry becomes one tool. When set, it takes precedence over the single-deployment variables.

[
  {
    "name": "summarize_document",
    "description": "Summarizes a document with the LLMGraph summarizer workflow",
    "endpoint": "https://llmgraph.ai/api/abc123/production",
    "apiKey": "your-api-key"
  },
  {
    "name": "support_bot",
    "description": "Asks the support assistant workflow a question",
    "endpoint": "https://llmgraph.ai/api/def456/production",
    "apiKey": "your-other-api-key",
    "inputSchema": "chat"
  }
]

Schema modes

  • input (default): the tool takes { "input": <object> } and the object is passed through unchanged as the POST body, so it works with any workflow input shape.

  • chat: for chat-style workflows. The tool takes { "user_input": <string>, "history": [{"role": "user"|"assistant", "content": <string>}] } (history optional) and sends it in the shape chat workflows expect.

Client setup

Claude Desktop

Add to claude_desktop_config.json (Settings, Developer, Edit Config):

{
  "mcpServers": {
    "llmgraph": {
      "command": "npx",
      "args": ["-y", "@llmgraph/mcp-server"],
      "env": {
        "LLMGRAPH_ENDPOINT": "https://llmgraph.ai/api/abc123/production",
        "LLMGRAPH_API_KEY": "your-api-key",
        "LLMGRAPH_TOOL_NAME": "summarize_document",
        "LLMGRAPH_TOOL_DESCRIPTION": "Summarizes a document with my LLMGraph workflow"
      }
    }
  }
}

Restart Claude Desktop and the tool appears in the tools menu.

Claude Code

claude mcp add llmgraph \
  --env LLMGRAPH_ENDPOINT=https://llmgraph.ai/api/abc123/production \
  --env LLMGRAPH_API_KEY=your-api-key \
  -- npx -y @llmgraph/mcp-server

Cursor

Add to ~/.cursor/mcp.json (or .cursor/mcp.json in your project):

{
  "mcpServers": {
    "llmgraph": {
      "command": "npx",
      "args": ["-y", "@llmgraph/mcp-server"],
      "env": {
        "LLMGRAPH_ENDPOINT": "https://llmgraph.ai/api/abc123/production",
        "LLMGRAPH_API_KEY": "your-api-key"
      }
    }
  }
}

Error handling

Non-200 responses from the LLMGraph API are returned to the client as MCP tool errors carrying the API's error message:

Status

Meaning

400

invalid request body

401

missing or invalid API key

402

subscription blocked

403

API disabled or origin not allowed

404

unknown deployment or wrong API key

422

workflow run failed

429

rate or budget limited

504

workflow timed out

Security notes

  • LLMGraph API keys are secrets for server-side use. This server sends the key only as the x-api-key header of requests to your configured endpoint, and never writes it to stdout, stderr, or error messages.

  • Client config files like claude_desktop_config.json store the key in plain text on your machine; treat them accordingly.

Development

npm install
npm run build   # compiles TypeScript to dist/
npm test        # builds, then runs unit tests (node --test), no network calls

License

MIT, see LICENSE.

Available Tools

1 tool
productionB

Invoke the LLMGraph workflow deployment "production"

ParametersJSON Schema
NameRequiredDescriptionDefault
inputYesJSON object sent as the workflow input, passed through as the request body

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only states that the deployment is invoked, without explaining side effects, return behavior, asynchronous execution, idempotency, or required permissions. This is a significant transparency gap for an action that likely triggers a workflow.

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 filler or redundant wording. Every word contributes to identifying the action and resource, making it highly effective for an agent scanning tool definitions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema and no annotations, the description omits important contextual details such as what the workflow returns, whether invocation is synchronous, and what side effects may occur. The input is well-covered, but the overall invocation experience is under-specified.

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?

Schema description coverage is 100%, and the input schema clearly explains that 'input' is a JSON object passed through as the request body. The description itself adds no parameter detail, but the schema already carries the semantic weight, so the baseline score of 3 applies.

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 a specific verb ('Invoke') and identifies a precise resource ('the LLMGraph workflow deployment "production"'), so an agent can tell exactly what the tool does. There are no sibling tools to distinguish against, and the resource is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool should be used when the agent needs to trigger the production deployment, but it does not explicitly state when to use it, when not to use it, or any prerequisites. With no siblings, the lack of alternative routing is acceptable, but the guidance remains only implied.

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

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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusing it with another. The tool name is unique within the server, so an agent can unambiguously select it.

Naming Consistency2/5

The single tool name 'production' is a noun rather than an action-oriented verb_noun pattern, and there are no other tools to establish a consistent convention. The name is vague and does not clearly indicate what operation it performs.

Tool Count1/5

A single tool named 'production' that only invokes a workflow deployment is extremely thin for a server, appearing more like a one-off action than a coherent toolset. This falls at the extreme low end of the count appropriateness scale.

Completeness1/5

The server exposes no other lifecycle operations around deployments—there is no way to list, create, update, or delete workflows, or to invoke any environment other than 'production'. This is a severely incomplete surface for any real workflow management purpose.

Maintenance

ActivitySlowing
ResponsivenessSyncing

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