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 CrewAI 10 tools — connect in under 2 minutes

Name: Semgrep
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Semgrep through the Vinkius — pass the Edge URL in the `mcps` parameter and every Semgrep tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Semgrep Specialist",
    goal="Help users interact with Semgrep effectively",
    backstory=(
        "You are an expert at leveraging Semgrep tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Semgrep "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Ed25519Audit chain

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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.

When paired with CrewAI, Semgrep becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Semgrep tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from Semgrep

Why Use CrewAI with the Semgrep MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Semgrep through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Semgrep + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Semgrep for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Semgrep, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Semgrep tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Semgrep against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Semgrep MCP Tools for CrewAI (10)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Semgrep + CrewAI FAQ

Common questions about integrating Semgrep MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.