How to Use the Greptile MCP in LlamaIndex

Q: What is the best way to filter tools?

You can use the allowedtools filter in your client spec to restrict the agent to only use specific Greptile functions. This prevents unnecessary indexing operations.

Q: How do I check if my indexing is finished?

Poll getrepositorystatus after you trigger an index. It will provide the current status of the repository, confirming when the data is ready for your search.

Build RAG applications with LlamaIndex using Greptile tools to ground your responses in real-time codebase data.

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Connect Greptile MCP to LlamaIndex

Create your Vinkius account to connect Greptile 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 Greptile with LlamaIndex

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Index codebases into LlamaIndex

Pipe repository data directly into your vector store using `index_repository`. This makes your codebase searchable through the same interfaces you use for your other documents. Once indexed, you can perform semantic searches across your files. Use `search_by_filepath` to narrow down the context for your RAG pipeline before generating a final answer.

Perform grounded queries with Greptile

Execute `query_with_context` to pull specific code insights into your LlamaIndex workflow. This ensures your AI doesn't just guess but references the exact state of your current files. This tool allows for precise retrieval where the agent receives the necessary snippets to form a grounded response. It removes the ambiguity common in standard LLM training data.

Manage your codebase knowledge base

Keep your indices clean by using `delete_repository` when a project is archived. You can audit your current index state at any time by calling `list_repositories`. This keeps your LlamaIndex knowledge base focused on active development. You avoid cluttering your vector store with outdated code that no longer reflects your current system architecture.

Setup guide

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

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Common questions about Greptile MCP in LlamaIndex

Greptile acts as the retrieval layer for your code. You query the codebase via tools, and LlamaIndex uses those results to augment the context provided to your LLM.

Yes, the tools are designed to handle massive codebases by indexing them before you ever run a query. This prevents timeouts during your actual retrieval phase.

All interactions are scoped to your specific API key. Your indexed codebase is treated as private data and is never exposed to other users or public models.

You can use the `allowed_tools` filter in your client spec to restrict the agent to only use specific Greptile functions. This prevents unnecessary indexing operations.

Poll `get_repository_status` after you trigger an index. It will provide the current status of the repository, confirming when the data is ready for your search.

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