DeepSource MCP for AI Agents. Monitor Code Quality and Security Vulnerabilities
DeepSource lets your AI client analyze code quality, find security flaws, and track complex metrics across repositories using natural language prompts. Instead of clicking through dashboards to check for bugs or high cyclomatic complexity, you just ask your agent. It pulls live data on everything from dependency vulnerabilities (CVEs) to overall repository health scores (A-F), giving instant reports without leaving your IDE.
Give Claude and any AI agent real-world access
Get a single, high-level report card for the repository that summarizes its overall quality status and identifies trends.
List detailed code issues, such as anti-patterns or unused variables, complete with file paths and line numbers.
Find dependency flaws by listing known CVE IDs, CVSS scores, and determining if the flaw is reachable in your code.
Retrieve specific quantitative data points like maintainability index, cyclomatic complexity, and test coverage percentages for comparison.
View a log of all past code analyses, including the branch name, analyzer used, and whether the run succeeded or failed.
Control which repositories are actively monitored by DeepSource, allowing you to pause analysis or update default branches as needed.
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What AI agents can do with 14 Tools in the DeepSource MCP for Code Quality Metrics
Use these tools through your agent to manage repositories, list specific issues, or retrieve deep code quality metrics like coverage percentages and complexity scores.
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Start using DeepSource MCPActivate Repository
Turns on deep source analysis for a repository that was previously paused or inactive, allowing code quality monitoring to start again.
Deactivate Repository
Stops all new analyses for a given repository, useful when archiving or temporarily...
Get Report Card
Provides an immediate, high-level grade (A-F) showing the overall code quality...
Get Repository Metrics
Retrieves specific quantitative data points for a repo, such as line coverage or...
Get Repository
Inspects the basic configuration details of a repository to confirm its identity...
Get Test Coverage
Shows the current test coverage percentage and checks it against configured quality thresholds for the codebase.
Get Viewer
Verifies that your API token is working correctly and retrieves basic user profile information from DeepSource.
Get Vulnerability
Deep dives into a single dependency flaw, providing detailed information about its...
List Analysis Runs
Lists the most recent code analysis attempts for a repo, showing which analyzer ran...
List Issues
Identifies specific code smells, anti-patterns, or potential bugs across the...
List Sca Targets
Lists all dependency manifest files (like npm or pip) that DeepSource is currently...
List Vulnerabilities
Generates a list of security flaws in your dependencies, detailing the severity, CVE ID, and fixability status.
Regenerate Dsn
Creates a brand new Data Source Name token for the repository to ensure continuous analysis runs are authenticated securely.
Update Default Branch
Changes the primary branch name that DeepSource uses as the default source of truth...
Security and governance baked right in.
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DeepSource MCP for AI Agents: Addressing Code Smell and Technical Debt
Today, catching code smells means running a static analysis tool, getting an output file, then opening another system to manually cross-reference those issues against the repository's current state. You spend time translating technical warnings into actionable development tasks.
With this MCP, you just ask your agent: 'What are the top 5 code smells in the payments module?' The agent reads the data and gives you a prioritized list of anti-patterns right away. It makes finding technical debt instant.
DeepSource MCP for AI Agents: Managing Dependency Vulnerability Risk
Manually managing security risk involves maintaining spreadsheets that track every dependency version and cross-referencing those against public CVE databases. This process is slow, reactive, and often misses the 'reachability' factor.
This MCP allows you to ask for a vulnerability report by listing all risks. It doesn't just list flaws; it tells you if the flaw is reachable in your code, letting your team focus only on the high-impact, active threats.
What DeepSource MCP for AI Agents MCP does for your AI
Stop navigating complex web dashboards just to grade a codebase. DeepSource connects code quality analysis and security scanning directly to your AI client, letting you review massive amounts of technical debt using simple conversation.
Your agent acts as an expert developer or dedicated security reviewer for your entire repository history. Need to know if the latest pull request introduced high cyclomatic complexity? Just ask. Are there any critical CVEs in the dependencies that need immediate patching? Your AI client pulls those details instantly.
It gives you a comprehensive view of code smells, anti-patterns, and deep metrics like test coverage percentages—all while remaining inside your workflow. When you subscribe through Vinkius, you connect once and gain access to this powerful analysis engine from any compatible agent, making DeepSource an indispensable part of the modern development stack.
019d7583-6eb9-7012-842b-8929580a1728 How to set up DeepSource MCP for AI Agents MCP
The bottom line is that you get deep code analysis reports without ever leaving your chat interface or opening the DeepSource web dashboard.
Connect your AI client to this MCP and enter your DeepSource Personal Access Token.
Ask your agent a specific question about the codebase, like 'What are the high-risk dependency vulnerabilities in the main branch?'
The MCP executes the necessary checks, pulls the data, and presents a clear summary of findings directly back through your conversation.
Who uses DeepSource MCP for AI Agents MCP
This MCP is for engineering teams who are tired of context switching. It helps Developers fix problems before merging, Security Teams prioritize CVEs immediately, and Engineering Managers get instant status checks across multiple repos.
You check code issues or metrics directly from your IDE to find bugs and quality problems right where you're coding, fixing them before they hit the main branch.
You monitor dependency vulnerabilities, using CVE details and CVSS scores to prioritize remediation efforts based on whether the flaw is actually reachable in your application logic.
You instantly review code quality grades or analysis status across multiple large repositories without having to manually open and sort through dozens of dashboards.
Benefits of connecting DeepSource MCP for AI Agents MCP
Review complex metrics like cyclomatic complexity or maintainability index directly from your agent, without opening the DeepSource dashboard.
Immediately identify code issues, such as unused imports or anti-patterns, using list_issues to pinpoint exact lines of problematic code.
Prioritize security fixes by listing vulnerabilities with CVE IDs and CVSS scores, allowing you to focus remediation efforts on high-reachability flaws.
Get an instant overall health grade via get_report_card, giving stakeholders a single, actionable metric for repository quality at a glance.
Understand your dependencies' risk surface area by using list_sca_targets to see exactly which manifest files are being scanned for supply chain threats.
DeepSource MCP for AI Agents MCP use cases
A security team needs an audit report on all critical flaws.
The agent runs list_vulnerabilities and filters the results, presenting a clear table of every CRITICAL CVE. The engineer then uses get_vulnerability to deep-dive into one specific issue, confirming the fix path before creating tickets.
A developer needs to know why their local code smells bad.
The developer asks the agent to check for issues and gets a list of problems. They then use get_repository_metrics to check the cyclomatic complexity score, confirming that a specific function is too complex and needs refactoring.
An engineering manager wants an instant health check across five repos.
The manager prompts for all report cards. The agent uses get_report_card multiple times in quick succession, providing a summary table of grades (A-F) and identifying the top three repositories needing immediate attention.
DevOps needs to adjust repository monitoring after a team migration.
The DevOps lead asks the agent to update the default branch using update_default_branch, ensuring that all future analyses run against the correct source code base (e.g., moving from 'master' to 'main').
DeepSource MCP for AI Agents MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Manually checking every single metric.
A team member opens the DeepSource dashboard, clicks on metrics, copies the cyclomatic complexity score, then has to open a separate tab to check test coverage. This is slow and error-prone.
Ask your agent to run get_repository_metrics; it pulls both the cyclomatic complexity and line coverage data into one conversational summary.
Forgetting to check dependency reachability.
A security team sees a HIGH CVSS score for an old library, flags it as critical, but doesn't know if the code actually uses that specific function. They waste time investigating a non-issue.
Use list_vulnerabilities and then get_vulnerability to check the 'reachability status,' confirming if the flaw is active in your current codebase.
Using outdated analysis tokens.
A new developer runs an analysis but it fails with an authentication error because the token was never rotated or regenerated, stopping all work until a manual fix.
Run regenerate_dsn first. This action invalidates the old token and provides a fresh one you can use to keep continuous monitoring running smoothly.
When to use DeepSource MCP for AI Agents MCP
Use this MCP if your primary need is converting deep, technical reports (metrics, CVEs, code smells) into conversational answers for your AI agent. This works best when you need quick comparisons or summaries of multiple data points; for example, comparing the cyclomatic complexity score against the overall report card grade.
Don't use this if you just need simple status checks on a single repository that don't involve deep code analysis. If you only need to know if a repo exists, using get_repository is fine, but it won't give you any quality insights. Also, if your security needs are limited to basic license checking and don't require CVE or CVSS scoring, then a simpler dependency checker tool might suffice instead.
Frequently asked questions about DeepSource MCP for AI Agents MCP
How do I get a DeepSource Personal Access Token and where do I find it? +
Log in to your DeepSource account, go to Account Settings → Personal Access Tokens, and click Create New Token. Give it a descriptive name (e.g., 'Vinkius MCP') and copy the token immediately — it won't be shown again. Paste this token into the API key field below. The token is used as a Bearer token in the Authorization header for all GraphQL requests to https://api.deepsource.com/graphql/.
What types of code issues can DeepSource detect and how are they categorized? +
DeepSource detects various code quality issues including code smells, anti-patterns, performance issues, security vulnerabilities, and bugs. Issues are categorized by severity (CRITICAL, HIGH, MEDIUM, LOW) and by analyzer type (e.g., PYTHON for Python issues, JS-A1 for JavaScript anti-patterns, GO for Go issues). Each issue includes a shortcode, title, category, and file locations with line numbers. You can filter issues by analyzer short code when querying repositories.
How does DeepSource detect dependency vulnerabilities and what information is provided? +
DeepSource uses Supply Chain Analysis (SCA) to scan dependency manifest files (package.json, requirements.txt, Gemfile, etc.) for known vulnerabilities. Each vulnerability includes: CVE ID, CVSS score (0-10), severity level, description, affected package name and version, ecosystem (npm, pip, etc.), reachability status (whether the vulnerable code is actually called), and fixability (whether a fix version is available). This helps prioritize which vulnerabilities to address first based on real risk rather than just theoretical severity.
What is the API rate limit and how many requests can I make per hour? +
DeepSource enforces a rate limit of 5,000 requests per hour per user account. This limit covers both read (queries) and write (mutations) operations. If you exceed this limit, the API will return HTTP 429 (Too Many Requests). For most code review and monitoring workflows, this limit is more than sufficient. If you need higher limits for large-scale analysis, contact DeepSource support.