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 Pydantic AI 10 tools — connect in under 2 minutes

Name: Semgrep
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Semgrep through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Semgrep "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Semgrep?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

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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.

Pydantic AI validates every Semgrep tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Semgrep MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Semgrep MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Semgrep integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Semgrep connection logic from agent behavior for testable, maintainable code

Semgrep + Pydantic AI Use Cases

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

Type-safe data pipelines: query Semgrep with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Semgrep tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Semgrep and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Semgrep responses and write comprehensive agent tests

Semgrep MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Semgrep to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Semgrep + Pydantic AI FAQ

Common questions about integrating Semgrep MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Semgrep MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.