Can the AI resolve or close findings in Semgrep natively?

Yes. This server supports mutable actions. By invoking `update_finding_status`, your AI agent can shift a specific semantic flaw to 'mitigated', 'fixed', 'ignored', or 'false_positive' updating the registry in real-time.

How can I deploy a new custom SAST rule via chat?

Simply ask the LLM: 'Draft a semantic grep rule to ban hardcoded API keys in Python and deploy it'. The agent will natively format the JSON structure required and call `create_rule`, sending it directly to all repositories.

Do I need to supply a 'Deployment Slug' for every request?

Most API queries require the deployment context. To ensure smooth interactions, just tell the agent your organization slug once (or let it query `list_deployments` to fetch the default one). The agent will remember it for the rest of the conversation loop.

Semgrep MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Semgrep
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Semgrep through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "semgrep": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Semgrep, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Semgrep through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Semgrep MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Semgrep via MCP

Why Use LangChain with the Semgrep MCP Server

LangChain provides unique advantages when paired with Semgrep through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Semgrep MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Semgrep queries for multi-turn workflows

Semgrep + LangChain Use Cases

Practical scenarios where LangChain combined with the Semgrep MCP Server delivers measurable value.

RAG with live data: combine Semgrep tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Semgrep, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Semgrep tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Semgrep tool call, measure latency, and optimize your agent's performance

Semgrep MCP Tools for LangChain (10)

These 10 tools become available when you connect Semgrep to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Semgrep to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Semgrep + LangChain FAQ

Common questions about integrating Semgrep MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.