Langbase MCP server

Connect Langbase to Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini, GitHub Copilot and any other MCP client, for your whole team. Metorial adds security, governance, and tracing, and gives everyone one Magic MCP URL to connect.

What you can do with the Langbase MCP server

Ask your agent in plain language. Each of these maps to a real tool on the server.

  • Use the list pipes tool in Langbase
  • Use the chunk text tool in Langbase
  • Use the append messages tool in Langbase
  • Use the create thread tool in Langbase
  • Use the list documents tool in Langbase
  • Use the run pipe tool in Langbase

Supported Tools

list_pipes

List Pipes

List all AI pipes (agents) in your Langbase account. Returns configuration details for each pipe including the model, status, and settings.

chunk_text

Chunk Text

Split text into smaller, manageable chunks. Useful for RAG pipelines, processing long documents, or working with specific sections of content. Uses intelligent text splitting with configurable chunk size and overlap.

append_messages

Append Messages

Append one or more messages to an existing conversation thread. Use this to add user, assistant, system, or tool messages to maintain conversation history.

create_thread

Create Thread

Create a new conversation thread in Langbase. Threads help organize and maintain conversation history across multiple interactions. You can optionally provide initial messages and a custom thread ID.

list_documents

List Documents

List all documents in a memory. Returns document names, processing status, file metadata, and chunking configuration for each document.

run_pipe

Run Pipe

Run an AI pipe to generate a response. Send messages to a configured pipe and receive the LLM completion. Supports conversation threading via threadId, variable substitution, and optional LLM provider key override.

update_thread

Update Thread

Update a conversation thread's metadata.

retry_document_embeddings

Retry Document Embeddings

Retry generating embeddings for a document that failed processing. Use this when a document's status is "failed" to re-trigger the embedding generation.

create_pipe

Create Pipe

Create a new AI pipe (agent) in Langbase. Pipes are configurable AI agents that can generate text, chat with users, and perform various AI tasks. You can configure the LLM model, system prompts, temperature, and attach memory for RAG-based responses.

list_messages

List Messages

List all messages in a conversation thread in chronological order (oldest first). Returns the full conversation history including roles, content, and metadata.

generate_embeddings

Generate Embeddings

Generate vector embeddings for text chunks. Useful for semantic search, text similarity comparisons, and NLP tasks. Supports embedding models from OpenAI and Cohere.

create_memory

Create Memory

Create a new memory (RAG knowledge base) in Langbase. Memory provides vector store, file storage, and semantic similarity search. After creating a memory, upload documents to it for retrieval-augmented generation.

generate_images

Generate Images

Generate AI images using various providers including OpenAI DALL-E, Together AI Flux, Google Imagen, and more. Requires an LLM provider API key for the image generation provider.

update_pipe

Update Pipe

Update an existing AI pipe's configuration. Modify the model, prompts, temperature, and other settings of a pipe.

list_memories

List Memories

List all memory (RAG knowledge bases) in your Langbase account. Returns the name, description, and embedding model for each memory.

delete_thread

Delete Thread

Delete a conversation thread and all its messages. This action is permanent and cannot be undone.

get_thread

Get Thread

Get details of a conversation thread including its metadata.

run_agent

Run Agent

Run the Langbase runtime LLM agent directly, specifying all parameters at runtime. Supports 100+ LLMs across all major providers (OpenAI, Anthropic, Google, etc.). Unlike pipes, the agent requires specifying the model and LLM provider key per request.

delete_document

Delete Document

Delete a document from a memory. This removes the document and its embeddings permanently.

delete_memory

Delete Memory

Delete a memory (RAG knowledge base) and all its documents from Langbase. This action is permanent and cannot be undone.

web_crawl

Web Crawl

Crawl web pages to extract their content using Spider Cloud or Firecrawl through Langbase. Provide URLs to crawl and receive extracted page content. Requires a crawl service API key.

retrieve_memory

Retrieve from Memory

Perform a semantic similarity search against one or more memories. Returns the most relevant text chunks based on the query. Useful for testing RAG retrieval and debugging chunking parameters.

upload_document

Upload Document

Upload or replace a document in a Langbase memory. Langbase returns a signed upload URL and the tool uploads the content to that URL, making the document available for memory processing and retrieval.

parse_document

Parse Document

Extract text content from a document using Langbase Parser. Useful before chunking text, embedding content, or loading a document into memory.

Connect Langbase to your AI client

Every client uses the same Magic MCP URL, and so does any other MCP client. Add it once and sign in with SSO.

  1. Enable Langbase in Metorial. In your Metorial portal, enable the Langbase MCP server and sign in with the account that owns the data. Access stays governed by your team policies.
  2. Add a custom connector. Open Customize, then Connectors. Select Add, then Custom, then Web.
  3. Paste your Metorial MCP URL. Name it Metorial, paste your Magic MCP URL (<your-magic-mcp-url>) and continue.
  4. Sign in. Select Add and sign in to load Langbase tools.

Run Langbase through the Metorial gateway

One governed layer for every app your agents reach, not a separate connector per tool.

  • Give Langbase and every other app to your whole team through one MCP gateway, behind your SSO.
  • Scope which teams and agents can use Langbase tools with role-based access control, and trace every call.
  • Prefer to run it yourself? Metorial is open source and self-hostable.

More integrations teams use with Langbase

Notion

Create, read, update, and archive pages and databases in a Notion workspace. Manage blocks (paragraphs, lists, headings, embeds, etc.) within pages. Query databases with filters and sorts across rich property types including text, numbers, dates, selects, relations, and formulas. Search across pages and databases by title. Add and read comments on pages and blocks. Upload files to pages. Manage workspace users and retrieve user profiles.

Google Sheets

Create, read, and update spreadsheets and their cell data. Read and write values to individual cells, ranges, or multiple ranges using A1 notation or named ranges. Apply cell formatting including text styles, backgrounds, borders, number formats, and conditional formatting. Manage sheets (tabs) within a spreadsheet—add, delete, copy, rename, and reorder them. Create and manage embedded charts, pivot tables, filter views, data validation rules, protected ranges, named ranges, and merged cells. Perform batch operations to apply multiple updates atomically.

GitHub

Manage repositories, issues, and pull requests. Create and configure branches, star repositories, review code, and merge changes. Automate CI/CD workflows with GitHub Actions, manage workflow runs, secrets, and artifacts. Track issues with labels, milestones, and assignees. Search across code, repositories, issues, and users. Manage organizations, teams, and memberships. Create and manage projects, gists, packages, deployments, and environments. Access security alerts including code scanning, secret scanning, and Dependabot alerts. Read and write file contents in repositories. Manage webhooks, notifications, and codespaces.

Salesforce

Manage CRM data including Accounts, Contacts, Leads, Opportunities, Cases, and custom objects. Create, read, update, and delete records. Query data using SOQL and search across objects using SOSL. Perform bulk data operations for large-scale imports, exports, and migrations. Execute composite requests to batch multiple operations in a single API call. Access analytics, reports, and dashboards. Manage files and attachments associated with records. Interact with Chatter feeds, posts, and groups for social collaboration. Manage org metadata including custom objects, fields, layouts, and workflows. Query data using GraphQL for precise data retrieval across related objects.

Sharepoint

Manage SharePoint sites, document libraries, lists, and files. Create, read, update, and delete lists and list items with custom columns. Resolve site users to numeric Person/Group LookupId values. Upload, download, move, copy, and version files in document libraries. Search across sites, files, folders, lists, and list items using Microsoft Search. Manage permissions at site, list, and item levels with granular access control. Define and manage content types and site columns. Retrieve site properties and search for sites across Microsoft 365.

Microsoft Outlook

Send, read, reply to, forward, and manage email messages in user mailboxes. Organize messages into folders, apply categories, flags, and importance levels. Manage file and item attachments. Create, update, delete, and respond to calendar events and meetings. Find available meeting times, manage attendees, handle recurrence, and work with shared or delegated calendars. Create, read, update, and delete contacts, organize them into contact folders, and manage contact photos. Manage tasks and task lists via Microsoft To Do, including due dates, reminders, recurrence, and checklist items. Support for Focused Inbox, @-mentions, mail tips, send-on-behalf, and send-as capabilities.

Technical notes for Langbase

Build and run AI agents using a unified LLM API across 600+ models. Create and manage pipes (AI agents) for text generation and chat. Store, upload, list, delete, and search documents using managed RAG memory with vector embeddings, chunking, and semantic similarity search. Manage conversation threads with full history and context. Parse and extract text from documents (PDFs, CSVs, etc.). Generate vector embeddings for text chunks. Generate AI images via multiple providers. Perform web search and web crawling through integrated tools. Supports tool calling, structured outputs, JSON mode, and vision-capable model inputs where supported by Langbase.

Frequently asked questions

Common questions about connecting Langbase to AI agents with Metorial.

  1. How many tools does the Langbase MCP server have?
    The Langbase MCP server exposes 25 tools, including List Pipes, Chunk Text, Append Messages. Each one is governed and traced through Metorial.
  2. Langbase exposes tools your agents call on demand; it does not publish event triggers yet. Everything still runs through one governed MCP URL.
  3. Any MCP client, including VS Code, Windsurf, Zed, OpenCode and Grok. Each one reaches the Langbase MCP server through the same Metorial URL.
  4. Use role-based access control to decide which teams, users, and agents can use Langbase and which of its tools they can call.
  5. Yes. Metorial is open source, so you can run the Langbase MCP server yourself or use the hosted gateway.

Connect Langbase to production AI agents

See how Metorial gives your team governed access to Langbase, with tracing and security controls built in.