How to Use the DeepSource MCP in LlamaIndex

Q: Can I query DeepSource test coverage trends in LlamaIndex?

Yes, by indexing the output of gettestcoverage over time. Your query engine reads the stored historical coverage values to answer complex natural language questions about your test suite health.

Q: What tool does LlamaIndex use to verify the DeepSource MCP Server connection?

The framework calls getviewer during initialization to confirm your API token is active. This returns your authenticated user profile and ensures the client can read your repositories.

Index your DeepSource code metrics and vulnerability scans into LlamaIndex to query your codebase health with natural language.

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Connect DeepSource MCP to LlamaIndex

Create your Vinkius account to connect DeepSource 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 DeepSource with LlamaIndex

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Feed DeepSource metrics directly into LlamaIndex RAG

The `get_repository_metrics` tool pulls live code quality indicators into your LlamaIndex vector store. Your index ingests metrics like maintainability index and line coverage, converting raw telemetry into searchable document nodes. When your agent queries this MCP Server, the engine retrieves these live metrics alongside your documentation. This ensures your agent's answers reflect the actual state of your codebase rather than outdated assumptions.

Search dependency vulnerabilities using this MCP Server

The `list_vulnerabilities` tool fetches active supply chain security risks for indexing by LlamaIndex. The tool retrieves CVE IDs, severity scores, and affected package versions, which the framework indexes as structured metadata. Your agent queries this index to find which services run vulnerable packages without making repeated API calls. If a risk is flagged, the agent uses `get_vulnerability` to pull deep-dive details and update the index with remediation steps.

Build a queryable history of analysis runs

The `list_analysis_runs` tool provides a continuous log of analyzer executions to your LlamaIndex pipeline. Each run's status, analyzer type, and branch name are indexed as chronological events. This allows you to run semantic searches over your build history to trace when specific analyzer failures started. Your agent can correlate these run failures with issues pulled from `list_issues` to find the exact commit that introduced a bug.

Setup guide

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

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Common questions about DeepSource MCP in LlamaIndex

You use the LlamaIndex MCP tool spec to connect the server, then call `list_issues` to retrieve code smells. LlamaIndex converts these issues into document nodes, making your static analysis findings fully searchable.

Yes, by indexing the output of `get_test_coverage` over time. Your query engine reads the stored historical coverage values to answer complex natural language questions about your test suite health.

Your credentials reside in the secure Vinkius environment, completely isolated from your LlamaIndex vector store. The server only passes raw data like report cards or issue lists to your indexing pipeline.

The framework calls `get_viewer` during initialization to confirm your API token is active. This returns your authenticated user profile and ensures the client can read your repositories.

Your dependency vulnerability reports and analyzer logs are processed in an ephemeral V8 sandbox. Vinkius ensures that this sensitive security telemetry is encrypted in transit and never stored on host disks.

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