FastGPT MCP for AI. Ground your AI answers using internal documents.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
FastGPT MCP lets you build sophisticated AI applications that talk directly to your company's specific knowledge base. This connector automates the entire data lifecycle—from ingesting raw documents and chunks of text to running advanced semantic searches and powering contextual chat completions using internal policies.
What your AI can do
Chat completions
Sends a message to a FastGPT application, tracking context and showing detailed intermediate steps for better debugging.
Get embeddings
Generates numerical representations (embeddings) for any piece of text, useful for external semantic search tests.
Get app detail
Pulls all configuration details for a single AI application, letting you check its status and linked datasets.
Create and manage dedicated datasets (knowledge bases) to hold specific types of company information.
Push text content or document chunks directly into a dataset for automatic indexing so the AI can read it.
Run advanced semantic queries against your knowledge base to find relevant information and test search quality.
List and get detailed configurations for the various AI applications linked to your datasets.
Trigger RAG-powered chat sessions that track conversation history, ensuring consistency throughout a multi-step discussion.
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FastGPT: 12 Tools for Data Management
Use these tools to create, update, search, and manage all aspects of your knowledge bases directly through your AI agent.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using FastGPT on VinkiusChat Completions
Sends a message to a FastGPT application, tracking context and showing detailed intermediate steps for better debugging.
Get Embeddings
Generates numerical representations (embeddings) for any piece of text, useful for...
Get App Detail
Pulls all configuration details for a single AI application, letting you check its...
Get Dataset Detail
Retrieves the full metadata and structure of an existing knowledge base dataset.
Create Dataset
Builds an entirely new knowledge base dataset so you can start storing specific...
Delete Dataset Data
Removes specific pieces of data from a dataset when the source documents are outdated or inaccurate.
List Apps
Lists all the AI applications you've created within your FastGPT environment.
List Dataset Data
Shows a list of the individual data items currently stored in a specific dataset.
List Datasets
Retrieves a list of all available knowledge bases, with options to filter by parent...
Push Dataset Data
Adds new content or modifies existing data within a dataset's structured records.
Search Dataset Data
Performs an advanced semantic search query against a dataset to find highly relevant...
Update Dataset Data
Modifies the content or metadata of existing data records in a knowledge base.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with FastGPT, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FastGPT. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 12 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Trying to build an AI agent from scattered corporate docs is a nightmare.
Right now, feeding an AI agent reliable answers means manual labor. You're constantly hopping between SharePoint, Google Drive, and local folders, copying chunks of text into prompt drafts just so the model can 'see' what you want it to know. It’s tedious, error-prone, and always out of date.
With this MCP, that process stops being manual. You build a centralized knowledge layer first. Then, your agent accesses that controlled source instead of relying on whatever data was available when the model was trained. The result is an AI that answers with authority.
FastGPT gives you full control over your knowledge base content.
You don't have to rely on just chat. You can use `list_datasets` to see what data sources exist, then use `get_dataset_detail` to validate the structure. If a policy changes, instead of rebuilding everything, you simply run `push_dataset_data` to update that specific record.
It moves your AI workflow from 'hope it knows' to 'it knows because we told it where.' You get guaranteed data lineage.
What your AI can actually do with this
Building an AI agent that actually knows your business requires more than just feeding it a prompt; you have to manage the source material itself. This MCP handles that whole process. You can programmatically create structured knowledge bases, then push in documentation—whether it's legal manuals or product specs—for automatic indexing and vectorization.
From there, your agent can run deep semantic queries against those specific datasets. It also lets you inspect applications and monitor the entire RAG pipeline directly from your workflow via Vinkius. The end result is an AI that grounds every answer in verified, structured data, eliminating generic responses and making it genuinely useful for day-to-day work.
019d843a-022b-7194-869a-b308ef104cb5 Here's how it actually works
The bottom line is you connect your existing AI workflow to a powerful data pipeline manager, letting it talk to structured internal knowledge sources.
First, subscribe to this MCP and set up your FastGPT instance (cloud or self-hosted).
Next, navigate to your app settings within FastGPT, generate an API Key, and find the Base URL.
Finally, enter both the API Key and Base URL into your AI client fields in Vinkius. You're ready to manage RAG pipelines.
Who is this actually for?
This MCP is for the technical specialists who treat data governance as seriously as they treat model performance. It's built for people whose job involves connecting messy enterprise documents to powerful, reliable AI.
They automate the ingestion of documentation across dozens of datasets, ensuring every source is correctly indexed and ready for retrieval.
They debug semantic search results, optimizing chunking strategies or checking if get_embeddings are working right before deployment.
They build complex AI workflows that require dynamically creating new knowledge bases or updating existing ones on the fly.
What Changes When You Connect
Data Integrity: When you need to update source material, use update_dataset_data or push_dataset_data. This ensures the AI is always referencing the most current version of a policy or document.
Debugging Search Failures: Don't guess why an answer was wrong. Use search_dataset_data to run precise semantic searches and verify that your knowledge base actually contains the right information.
System Visibility: You can track everything with list_apps. This lets you see all the connected AI applications and know exactly which datasets feed into them, simplifying audits.
Building From Scratch: Need a new area of expertise? Use create_dataset to build a dedicated knowledge base from scratch, keeping specialized data separate from general documentation.
Deep Context Chat: Instead of generic chat, use the chat_completions tool. It tracks session history and shows intermediate steps, making complex conversations reliable.
See it in action
Legal team needs to check compliance policies.
The legal analyst uses their agent to search for 'data retention policy' across multiple datasets. They use search_dataset_data and get specific citations, preventing them from citing outdated or incorrect rules.
Dev team is building a new product feature.
The developer uses their agent to check the current list of available apps via list_apps, then checks the details for a specific dataset using get_dataset_detail before connecting it to the chat function.
Operations team updates product manuals.
The ops manager uploads new versions of technical guides. Instead of manual file replacement, they use push_dataset_data and specify the document ID, ensuring a clean, traceable update to the knowledge base.
The honest tradeoffs
Treating documents as simple text inputs
Just pasting a huge block of copied policy text into an AI prompt and expecting it to perform accurate retrieval.
First, run create_dataset for that specific topic. Then, use push_dataset_data or the appropriate ingestion tool to structure and index the data correctly before querying.
Assuming all data is equally structured
Trying to query a dataset using a general search when some of its records are unstructured meeting notes.
Use list_datasets to identify and segment your data. If the information type changes, you need to create a new specialized knowledge base via create_dataset.
When It Fits, When It Doesn't
Use this MCP if your AI workflow relies on complex, multi-source corporate documents (e.g., manuals, policies, financial reports) and requires deep semantic understanding. You MUST use it if you need to manage the lifecycle of the data itself, not just run a single query.
Don't use this if all you need is simple Q&A based on 50 static FAQs. For basic knowledge retrieval without complex dataset management needs, simpler FAQ tools might suffice. But if your documents are messy or change often, this MCP provides the necessary control to manage that chaos.
Questions you might have
Can I use this with my self-hosted FastGPT instance? +
Yes! Simply provide your custom domain in the Base URL field (e.g., https://fastgpt.mycompany.com). The MCP will route all requests to your specific instance.
How do I add new data to an existing dataset? +
Use the push_dataset_data tool. You can send a JSON array of objects containing the text content to be indexed. FastGPT will handle the chunking and embedding automatically.
What is the 'goods_sign' used for in Pinduoduo tools? +
Wait, this is the FastGPT FAQ. For FastGPT, you mostly need Dataset IDs and App IDs. The 'goods_sign' is specific to the Pinduoduo MCP. Always check you are using the correct tools for the specific platform.
What credentials do I need to use the `get_app_detail` tool? +
You must provide your API Key and Base URL. These two parameters authenticate your connection to FastGPT, ensuring that all operations—like viewing app configurations—are securely tied back to your specific account.
If I want to remove old records, is the `delete_dataset_data` tool safe? +
It's highly effective for removal, but you must specify both the dataset ID and the exact data identifiers. This process deletes content permanently, so always verify your target IDs before running a deletion command.
How does `search_dataset_data` find relevant information? +
The tool uses advanced semantic embeddings rather than simple keyword matching. It analyzes the meaning and context of your query, returning results ranked by relevance score for complex questions.
Should I use `list_datasets` first to see all my available knowledge bases? +
Yes. Running list_datasets retrieves every dataset ID and name configured in your FastGPT instance. This lets you quickly confirm which knowledge base is ready for data ingestion or searching.
What happens if I need to correct existing information using the `update_dataset_data` tool? +
The tool overwrites the entire record associated with a specific data ID. You must provide that unique ID and the full, corrected content; partial updates aren't supported.
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