Can I see the quality grade for a specific repository?

Yes! Use the `get_repository_quality_analysis` tool. Provide the provider (e.g., `gh`), organization, and repository name. The agent will return the current letter grade (A-F) and core quality metrics.

How do I search for security-related issues in my code?

Use the `search_repository_issues` tool. You can filter the payload to include `categories: ['Security']`. Your agent will fetch all security vulnerabilities identified by Codacy in that repository.

Where do I find my Codacy API Token?

Log in to Codacy, click on your avatar in the top right, go to **User Settings**, and select the **API** tab. You can generate and copy your account token from there.

Codacy MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: Codacy
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Codacy through Vinkius, pass the Edge URL in the `mcps` parameter and every Codacy 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

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Codacy Specialist",
    goal="Help users interact with Codacy effectively",
    backstory=(
        "You are an expert at leveraging Codacy 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 Codacy "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

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

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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 Codacy MCP Server

Connect your Codacy account to any AI agent and take full control of your automated code reviews and quality metrics through natural conversation. Streamline how you monitor security and maintainability across your repositories natively.

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

What you can do

Organization Oversight — List and retrieve details for all organizations associated with your Codacy account natively
Repository Intelligence — Access current quality grades, complex files, and overall metrics for any analyzed repository flawlessly
Issue Management — Search for specific code quality issues using advanced filters like level, category, and language securely
Language Logistics — List all programming languages supported by the Codacy analysis engine flawlessly
Member Management — Access organization member rosters and user profile information securely
Webhook Visibility — Monitor configured webhooks for real-time quality and analysis notifications directly within your workspace

The Codacy MCP Server exposes 8 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 Codacy to CrewAI via MCP

Follow these steps to integrate the Codacy 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 8 tools from Codacy

Why Use CrewAI with the Codacy MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Codacy 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 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

Codacy + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Codacy 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 Codacy, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Codacy 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 Codacy against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Codacy MCP Tools for CrewAI (8)

These 8 tools become available when you connect Codacy to CrewAI via MCP:

get_my_codacy_profile

Retrieve information about the authenticated Codacy user

get_repository_quality_analysis

Get the current quality grade and metrics for a specific repository

list_codacy_organizations

List all organizations associated with the account

list_organization_members

List people and users belonging to an organization

list_organization_repositories

List all repositories analyzed within an organization

list_repository_webhooks

List configured webhooks for quality notifications

list_supported_languages

List programming languages supported by the Codacy analysis engine

search_repository_issues

Search for specific code quality issues in a repository

Example Prompts for Codacy in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Codacy immediately.

"List all repositories in the 'vinkius' organization on GitHub."

"Show me the security issues for the 'core-api' repository."

"What languages does Codacy support?"

Troubleshooting Codacy MCP Server with CrewAI

Common issues when connecting Codacy 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

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

Codacy + CrewAI FAQ

Common questions about integrating Codacy 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.