Semgrep MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Semgrep as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Semgrep. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Semgrep?"
)
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 Semgrep MCP Server
Connect the Semgrep AppSec platform directly to your AI agent to radically accelerate code security triaging. Instead of forcing developers to jump between their IDE and the Semgrep dashboard, empower your AI to pull 'Findings', analyze the vulnerable syntax, and instantly close false positives.
LlamaIndex agents combine Semgrep tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Triage Findings (Bugs) — Instruct the agent to grab the latest CI vulnerability findings and immediately push a status update to mark it as fixed, ignored, or mitigated (
update_finding_status) - Rule Management — Request the AI to look at a newly discovered bad coding pattern and command it to write and deploy a matching custom semantic rule (
create_rule) to your organizational deployment - Project & Deployment Scoping — Map out all repositories running Semgrep actions and check their overarching security health scores in milliseconds
- Comprehensive Forensics — Fetch granular SCA and SAST semantic flaw definitions, including exact snippets, CVE links, and the specific bad lines causing the trigger
The Semgrep MCP Server exposes 10 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.
How to Connect Semgrep to LlamaIndex via MCP
Follow these steps to integrate the Semgrep MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Semgrep
Why Use LlamaIndex with the Semgrep MCP Server
LlamaIndex provides unique advantages when paired with Semgrep through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Semgrep tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Semgrep tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Semgrep, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Semgrep tools were called, what data was returned, and how it influenced the final answer
Semgrep + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Semgrep MCP Server delivers measurable value.
Hybrid search: combine Semgrep real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Semgrep 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 Semgrep for fresh data
Analytical workflows: chain Semgrep queries with LlamaIndex's data connectors to build multi-source analytical reports
Semgrep MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Semgrep to LlamaIndex via MCP:
create_rule
Allows developers to forbid project-specific bad patterns securely and continuously across the enterprise repositories. Create a customized Semgrep security rule within the platform
delete_rule
Delete a custom Semgrep security rule from the deployment
get_finding_details
Explains the exact malicious code block, suggests semantic fixes, states whether it is blocking PRs in CI, and links to CVE data (if an SCA supply chain defect). Get atomic details for a specific Semgrep flaw
get_metrics
Typically consumed to render executive security dashboards. Get AppSec metrics and compliance stats for Semgrep
get_project
Search for a precise Semgrep project by exact repository name
list_deployments
The primary key is the deployment slug identifier. Almost all subsequent API operations targeting rules, projects, or findings will require this deployment slug to define the scope. List Semgrep organizational deployments
list_findings
Findings provide snippet details, file line numbers, severity, and rule types. Fetch global static analysis security findings for a deployment
list_projects
Projects maintain a link between developers and static security scan outputs over time. List Semgrep projects (repositories) monitored in a deployment
list_rules
The rules are structured YAML definitions that search for semantic anti-patterns in codebases (e.g., unparameterized SQL queries, hardcoded AWS keys). List Semgrep semantic rules deployed globally
update_finding_status
Valid states generally include active, fixed, false_positive, ignored, mitigated. Resolving findings through this API cleans up the developer experience when managing compliance queues. Mark a Semgrep finding state (e.g., fixed, false positive)
Example Prompts for Semgrep in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Semgrep immediately.
"List the most severe unmitigated findings currently breaking our CI/CD pipeline on the 'vinkius/cloud' repository."
"Mark vulnerability issue ID #58032 as a 'false_positive' using the update finding tool."
"Review the company's Semgrep performance metrics focusing on fix rate."
Troubleshooting Semgrep MCP Server with LlamaIndex
Common issues when connecting Semgrep to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSemgrep + LlamaIndex FAQ
Common questions about integrating Semgrep MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Semgrep with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Semgrep to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.