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.

FastGPT MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: FastGPT
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FastGPT as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to FastGPT. "
            "You have 12 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in FastGPT?"
    )
    print(response)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About FastGPT MCP Server

Connect your AI workflows to FastGPT, the powerful open-source platform for building knowledge-based AI applications. This MCP provides 12 tools for full lifecycle management of datasets, apps, and RAG (Retrieval-Augmented Generation) pipelines.

LlamaIndex agents combine FastGPT tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Dataset Orchestration — Create, list, and manage knowledge base datasets with granular control over configurations
Document Ingestion — Push text content or chunks directly to datasets for automatic indexing and vectorization
Semantic Search — Run advanced semantic queries against your knowledge bases to test relevance and RAG quality
Application Management — List and inspect AI applications to monitor their configurations and linked datasets
OpenAI-Compatible Chat — Trigger RAG-powered chat completions with full context, session tracking, and intermediate step visibility

The FastGPT MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect FastGPT to LlamaIndex via MCP

Follow these steps to integrate the FastGPT MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from FastGPT

Why Use LlamaIndex with the FastGPT MCP Server

LlamaIndex provides unique advantages when paired with FastGPT through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine FastGPT tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain FastGPT tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query FastGPT, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what FastGPT tools were called, what data was returned, and how it influenced the final answer

FastGPT + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the FastGPT MCP Server delivers measurable value.

Hybrid search: combine FastGPT real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query FastGPT to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying FastGPT for fresh data

Analytical workflows: chain FastGPT queries with LlamaIndex's data connectors to build multi-source analytical reports

FastGPT MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect FastGPT to LlamaIndex via MCP:

chat_completions

Supports chatId for context tracking, streaming, and detailed intermediate steps. Send a message to a FastGPT application

create_dataset

Create a new dataset (knowledge base)

delete_dataset_data

Delete specific data from a dataset

get_app_detail

Get details for a specific AI application

get_dataset_detail

Get details for a specific dataset

get_embeddings

Useful for semantic search outside of FastGPT. Generate text embeddings

list_apps

List AI applications

list_dataset_data

List data items in a dataset

list_datasets

Can filter by parentId or search keyword. List knowledge base datasets

push_dataset_data

Add or update data in a dataset

search_dataset_data

Perform semantic search on a dataset

update_dataset_data

Update existing data in a dataset

Example Prompts for FastGPT in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with FastGPT immediately.

"List all my AI applications in FastGPT."

"Search dataset 'ds_123' for 'company refund policy'."

"Create a new dataset named 'Internal Documentation'."

Troubleshooting FastGPT MCP Server with LlamaIndex

Common issues when connecting FastGPT to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

FastGPT + LlamaIndex FAQ

Common questions about integrating FastGPT MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query FastGPT tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.