How to Use the Cody AI MCP in LlamaIndex

Ground your LlamaIndex knowledge base in real-time Cody AI data and bot interactions.

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Connect Cody AI MCP to LlamaIndex

Create your Vinkius account to connect Cody AI 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 Cody AI with LlamaIndex

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Index Cody AI tools in LlamaIndex

Transform `list_documents` and `list_folders` outputs into searchable vector embeddings. LlamaIndex ingests these results to provide context for your RAG queries. Stop relying on static files. Your index stays current because the agent pulls the latest state directly from your account.

Build RAG with Cody AI MCP Server

The `McpToolSpec` allows your agent to fetch bot details or conversation logs on demand. It turns raw API responses into indexed knowledge. You avoid hallucinations by grounding answers in actual account data. If the information isn't in your Cody AI environment, the index reflects that gap.

Control Cody AI access with filters

Use the allowed_tools filter to restrict what the index can touch. You decide which operations are safe for automated retrieval. Accessing your data is straightforward with `include_resources=True`. This gives the agent a clear path to pull necessary content for your queries.

Setup guide

Set up Cody AI 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 Cody AI 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 Cody AI tools.",
)
response = await agent.run("List recent Cody AI data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cody AI. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cody AI now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cody AI MCP in LlamaIndex

You use the MCP tool spec to wrap the server functions. Once connected, your agent treats your Cody AI account as a native data source.

Yes, you can pull conversation history via the server tools and inject it into your index. This allows for semantic search across past bot interactions.

It relies on the standard MCP interface to ensure consistent tool execution. You get predictable schema mapping for every available function.

We use scoped tokens that only allow the tools you explicitly enable. Access is limited to the specific endpoints required for your indexing tasks.

The server retrieves text content only when requested by your agent. This data is processed in memory and never stored on our servers.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python