Greptile MCP Server for LlamaIndexGive LlamaIndex instant access to 11 tools to Delete Repository, Get File Info, Get Greptile Usage, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Greptile as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Greptile app connector for LlamaIndex is a standout in the Developer Tools category — giving your AI agent 11 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Greptile. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Greptile?"
)
print(response)
asyncio.run(main())
* 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 Greptile MCP Server
Connect your Greptile account to any AI agent and unlock AI-powered codebase understanding through natural conversation.
LlamaIndex agents combine Greptile tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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
- AI Codebase Q&A — Ask natural language questions about one or more repositories and receive AI-generated answers with code references
- Contextual Follow-ups — Continue conversations with session context for multi-turn codebase exploration
- Semantic Code Search — Search across indexed repositories to find relevant files, functions, and code patterns
- File-Specific Search — Target searches within a specific file path for precise results
- Repository Indexing — Submit GitHub or GitLab repositories for indexing, check progress, and trigger re-indexing
- Repository Management — List all indexed repos, inspect file metadata, and remove outdated indexes
- Usage Monitoring — Track API consumption and rate limits
The Greptile MCP Server exposes 11 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.
All 11 Greptile tools available for LlamaIndex
When LlamaIndex connects to Greptile through Vinkius, your AI agent gets direct access to every tool listed below — spanning codebase-intelligence, semantic-search, repository-indexing, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Delete indexed repository
Get file info
Check API usage
Get repository status
Index a repository
List indexed repositories
Query codebase with AI
Query with session context
Reindex a repository
Search in specific file
Search codebase
Connect Greptile to LlamaIndex via MCP
Follow these steps to wire Greptile into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Greptile MCP Server
LlamaIndex provides unique advantages when paired with Greptile through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Greptile tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Greptile tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Greptile, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Greptile tools were called, what data was returned, and how it influenced the final answer
Greptile + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Greptile MCP Server delivers measurable value.
Hybrid search: combine Greptile real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Greptile 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 Greptile for fresh data
Analytical workflows: chain Greptile queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Greptile in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Greptile immediately.
"How does the authentication middleware work in our backend repository?"
"Search for all files that import the database connection module and show me the file info."
"Index our new frontend repository and check the indexing status."
Troubleshooting Greptile MCP Server with LlamaIndex
Common issues when connecting Greptile to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGreptile + LlamaIndex FAQ
Common questions about integrating Greptile MCP Server with LlamaIndex.