Find Codebase Duplications Using MCP Servers.
Your codebase has 4 different implementations of date formatting, 3 versions of the retry logic, and 2 competing validation libraries , but nobody knows because grep only finds exact matches and these duplicates are semantic
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent queries Weaviate for vector embeddings of your codebase , functions, classes, and modules that have been indexed as vectors.
It runs similarity searches to find code blocks with high semantic similarity but different text: 'formatDate() in utils/dates.js and renderTimestamp() in components/Timeline.tsx are 94% semantically similar , both convert ISO 8601 to locale string with timezone adjustment.
Different function names, different files, same logic.' The agent reads both files from GitHub to verify the duplication and assess which implementation is better documented, tested, and maintained.
Then it creates a Linear ticket: 'Refactor: consolidate date formatting. 4 implementations found across 3 repositories. Recommended canonical: utils/dates.ts (has tests, handles edge cases).
Remove: Timeline.tsx inline version, billing/format.js, api/helpers/time.js. Estimated effort: 2 story points. Risk: low (all 4 produce identical output).' Code duplication that lives for years because nobody can search for concepts , only exact text matches , gets surfaced and resolved.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Weaviate, GitHub and Linear MCP servers so your AI agent uses vector search on your Weaviate instance to find semantically similar code blocks across your repositories, identifies conceptual duplication that text search cannot find, and creates refactoring tickets in Linear with the duplicated code pairs and consolidation recommendations. Engineering teams with codebases over 100K lines where grep finds nothing but the same logic exists in 5 places with different variable names , and every bug fix needs to be applied in all 5 places without anyone knowing where they all are , get a semantic X-ray that finds conceptual debt invisible to traditional search.
Weaviate
triggerRuns vector similarity search to find semantically similar code blocks across the codebase
search_near_vector list_objects get_object_details get_class_schema Github
enrichmentReads the actual source files to verify duplication and identify code ownership
get_file_contents search_github_code get_repository_details list_pull_requests Linear
actionCreates prioritized refactoring tickets with duplication pairs, impact analysis and consolidation plan
create_issue list_issues list_teams list_labels Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Weaviate, Github & Linear ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
Engineering teams with large codebases who want to find semantic code duplication invisible to grep and IDE search
Platform teams establishing shared libraries who need to identify consolidation candidates across microservices
Tech leads conducting codebase health audits who want quantified duplication metrics with refactoring recommendations
Teams migrating from monolith to microservices who need to identify code that should be extracted into shared packages
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: Weaviate, GitHub and Linear. Connect all three to your AI client. Your codebase must be indexed as vector embeddings in Weaviate , use a code embedding model like CodeBERT or similar.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others. Connect the MCP servers and paste a prompt.
How do I index my codebase in Weaviate?
Parse your code into functions and classes, generate embeddings using a code-specific model, and store them in Weaviate with metadata (file path, function name, language). The agent searches these embeddings for similarity.
Is my code secure?
MCP servers authenticate through API keys. Weaviate and GitHub data stays in your infrastructure. Linear tickets contain references, not full source code. Vinkius does not access your code.
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MCP servers used in this workflow
Weaviate
Weaviate MCP Server lets your AI client interact directly with a vector database. You can run semantic searches against massive collections, check the health of your cluster nodes, and manage schemas—all through natural conversation. It bypasses complex console UIs for data discovery and retrieval.
GitHub
GitHub MCP Server manages repositories, tracks issues, and searches code via AI agents. Connect your GitHub account to your preferred AI client and automate core developer workflows—listing repos, getting file contents, or creating new issues—all from a natural conversation. Manage your entire software development lifecycle without leaving your chat window.
Linear
Linear lets your AI client read, write, and manage issues directly inside Linear—no tab switching needed. You can list all teams, search for specific bugs, create new tasks with defined priorities, or add comments right from your IDE. It gives your agent full control over project metadata, allowing you to check sprint progress, view project scope, and audit issue status using natural conversation.