Convert PDFs and other documents to structured formats via Docling, for AI applications.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β we're steadily working through the catalog.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
A document processing service using the Docling-MCP library and MCP (Model Context Protocol) for tool integration.
Docling MCP is a service that provides tools for document conversion, processing and generation. It uses the Docling library to convert PDF documents into structured formats and provides a caching mechanism to improve performance. The service exposes functionality through a set of tools that can be called by client applications.
| docling-mcp | MCP Python SDK |
|---|---|
>=3.0.0 | mcp>=2.0.0 |
>=2.0.0,<3.0.0 | mcp>=1.9.4,<2.0.0 |
If your MCP client application has not yet migrated to MCP SDK v2, pin:
See MIGRATION.md for the full migration guide.
For users with access to Docling Serve API:
Getting Docling Serve: Visit docling-serve for installation guides. You can deploy it from published container images or look for managed Docling SaaS offerings.
Then configure your environment:
For users who need local conversion or don't have Docling Serve access:
Then configure your environment:
Install with local support and enable automatic fallback:
Configure for remote with fallback:
All settings use the DOCLING_MCP_ prefix and can be supplied as environment
variables, in a .env file in the working directory, or via the env block of
your MCP client config. Copy .env.example as a starting point.
| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_CONVERSION_MODE | remote | remote or local |
DOCLING_MCP_CONVERSION_MODE=remote)| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_SERVICE_URL | β | URL of the Docling Serve instance |
DOCLING_MCP_SERVICE_API_KEY | β | API key for the service |
DOCLING_MCP_SERVICE_TIMEOUT | 300.0 | Timeout in seconds for a whole conversion job |
DOCLING_MCP_SERVICE_MAX_RETRIES | 3 | Max retry attempts |
DOCLING_MCP_FALLBACK_TO_LOCAL | false | Fall back to local if service is unreachable (requires docling-mcp[local]) |
| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_KEEP_IMAGES | false | Retain page images in output |
DOCLING_MCP_IMAGES_SCALE | 1.0 | Image scale factor (increase to avoid tensor padding errors) |
DOCLING_MCP_DO_OCR | true | Run OCR pipeline |
DOCLING_MCP_DO_TABLE_STRUCTURE | true | Detect table structure |
| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_IMAGE_EXPORT_MODE | placeholder | How images are rendered in Markdown output: placeholder (emits <!-- image -->), embedded (base64 data-URI), referenced (file path / URL) |
--tools llama-index-rag)| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_LI_API_BASE | http://127.0.0.1:1234/v1 | OpenAI-compatible LLM endpoint |
DOCLING_MCP_LI_API_KEY | none | API key for the LLM endpoint |
DOCLING_MCP_LI_MODEL_ID | ibm/granite-3.2-8b | LLM model identifier |
DOCLING_MCP_LI_EMBEDDING_MODEL | BAAI/bge-base-en-v1.5 | HuggingFace embedding model |
--tools llama-stack-rag / --tools llama-stack-ie)| Variable | Default | Description |
|---|---|---|
DOCLING_MCP_LLS_URL | http://localhost:8321 | LlamaStack server URL |
DOCLING_MCP_LLS_VDB_EMBEDDING | all-MiniLM-L6-v2 | Embedding model for vector DB |
DOCLING_MCP_LLS_EXTRACTION_MODEL | openai/gpt-oss-20b | Model used for structured extraction |
The easiest way to install Docling MCP and connect it to your client is by launching it via uvx.
Depending on the transfer protocol required, specify the argument --transport, for example
stdio used e.g. in Claude for Desktop and LM Studio
sse used e.g. in Llama Stack
streamable-http used e.g. in containers setup
More options are available, e.g. the selection of which toolgroup to launch. Use the --help argument to inspect all the CLI options.
For developing the MCP tools further, please refer to the Developing section of CONTRIBUTING.md for instructions.
One of the easiest ways to experiment with the tools provided by Docling MCP is to leverage an AI desktop client with MCP support. Most of these clients use a common config interface. Adding Docling MCP in your favorite client is usually as simple as adding the following entry in the configuration file.
When using Claude for Desktop, simply edit the config file claude_desktop_config.json with the snippet above or the example provided here.
In LM Studio, edit the mcp.json file with the appropriate section or simply click on the button below for a direct install.
Other integrations are described in the [integrations] page.
Example of prompt for converting PDF documents:
Example of prompt for generating new documents:
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