How to Use the Fastn MCP in LlamaIndex

Q: Can LlamaIndex search through my historical Fastn execution traces?

Yes. By calling listexecutions and getexecution, LlamaIndex retrieves past run details, converts them into documents, and indexes them for semantic search queries.

Q: Does the Fastn MCP Server support real-time execution from LlamaIndex?

Yes. The agent uses executeflow to run workflows in real-time, allowing your query engine to execute live backend tasks based on user prompts.

Q: How do I manage API quotas when indexing large numbers of workflows?

You can monitor your request rates by calling getquotadaily and getquotausage, helping you rate-limit your indexing pipeline to avoid hitting platform caps.

Index your Fastn workflow definitions and execution histories directly into LlamaIndex vector stores using this MCP Server.

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Connect Fastn MCP to LlamaIndex

Create your Vinkius account to connect Fastn to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Fastn with LlamaIndex

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Index Fastn workflow schemas into LlamaIndex

Fastn MCP Server tools feed workflow metadata directly into your semantic index. The agent queries `list_workflows` and `get_workflow` to map out your entire automation library. This metadata becomes searchable documents within your index. Your RAG pipeline uses this data to answer questions about what your automations do and how they are configured.

Ground agent decisions in actual execution histories

Your LlamaIndex knowledge base pulls step-by-step trace data using `list_executions` and `get_execution`. Your query engine analyzes past runs to diagnose recurring issues or summarize system performance. When errors occur, the agent searches these historical traces. It quickly identifies whether a failure matches a known pattern and suggests the correct fix.

Run and monitor workflows from your query engine

Your agent triggers automated tasks directly from a query response using `execute_flow`. The agent matches the user's intent to a specific flow schema, formats the payload, and runs it instantly. If a run hangs, the agent uses `cancel_execution` to stop it. This keeps your query loop fast and prevents runaway processes from consuming your execution budget.

Setup guide

Set up Fastn MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Fastn MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Fastn tools.",
)
response = await agent.run("List recent Fastn data")

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Common questions about Fastn MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate the basic client. Use McpToolSpec to load the Fastn tools and pass them to your function-calling agent.

Yes. By calling list_executions and get_execution, LlamaIndex retrieves past run details, converts them into documents, and indexes them for semantic search queries.

Yes. The agent uses execute_flow to run workflows in real-time, allowing your query engine to execute live backend tasks based on user prompts.

You can monitor your request rates by calling get_quota_daily and get_quota_usage, helping you rate-limit your indexing pipeline to avoid hitting platform caps.

All execution traces and workflow definitions retrieved via get_execution are transmitted over secure local MCP channels. Your underlying connector credentials remain locked inside Fastn, accessible only through secure references rather than raw text.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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