Greptile MCP for AI. Understand any code base with natural conversation.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Greptile connects your AI agent directly to private codebases (GitHub, GitLab). Ask natural language questions about any repository—like asking 'How does the auth flow work?'—and get instant answers complete with file paths and line numbers.
It's a technical deep dive tool that lets you understand massive or unfamiliar code in minutes.
What your AI can do
Delete repository
Removes an indexed repository from the system.
Get file info
Retrieves specific details about a single file in your codebase.
Get repository status
Gets the current status of a repository index (e.g., processing, complete).
Ask natural language questions about your indexed code and receive accurate, referenced answers.
Find relevant functions, files, or patterns across entire repositories using abstract concepts instead of keywords.
Run searches restricted to a single file path when you know exactly where the code lives.
List, inspect metadata for, and manage your indexed repositories.
Submit new GitHub or GitLab repos for indexing or trigger a full re-scan of existing code.
Ask an AI about this
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Greptile MCP - 11 Tools Available
This suite of tools lets you manage your codebase indexes, check status, delete data, and perform complex searches across all connected repositories.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Greptile on VinkiusDelete Repository
Removes an indexed repository from the system.
Get File Info
Retrieves specific details about a single file in your codebase.
Get Repository Status
Gets the current status of a repository index (e.g., processing, complete).
Get Greptile Usage
Checks how much API usage you've consumed for billing purposes.
Index Repository
Starts the process of adding a new GitHub or GitLab repository to your searchable...
List Repositories
Shows you all the repositories that are currently indexed and available for querying.
Query Codebase
Asks an AI question against the entire codebase to get a conceptual answer with code references.
Query With Context
Continues an ongoing conversation, allowing subsequent questions to build on...
Reindex Repository
Triggers a full re-scan of an existing repository if the code has changed or needs...
Search By Filepath
Runs a search that is limited strictly to files matching a specific path.
Search Codebase
Performs a broad, semantic search across the codebase for relevant code patterns or...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. 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
Make Your AI Do More
Start with Greptile, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Greptile. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The sheer overhead of understanding unfamiliar codebases is brutal.
Right now, figuring out a new project means clicking through dozens of folders and service layers. You copy boilerplate function names into Google, read five different READMEs that contradict each other, and spend hours just mapping the dependency graph before you even write one line of code.
With this MCP, your agent reads everything for you. You simply ask it what you want to know—'How is user data validated?'—and the system returns a direct answer with code references, cutting days of research into minutes.
Greptile gives you instant visibility into every function and file.
You never have to manually run `list_repositories` just to see what's available, or waste time checking if the index is up-to-date. The system handles all that background work so you can focus on the architecture itself.
The result isn't just an answer; it’s a guided tour of your codebase. It shows you the context and links to every relevant file, making massive projects feel small.
What your AI can actually do with this
You can connect your AI agent to Greptile and treat your entire collection of private repositories like a searchable document. Instead of spending hours digging through READMEs or jumping between services, just ask your agent what it needs to know—whether it's how the database connection is handled or which files manage user permissions.
The system reads every indexed file and answers in plain English, pointing you right to the code that matters. This entire service lives inside Vinkius, letting you connect once from any compatible client and get access to this codebase intelligence alongside hundreds of other tools. You can even track usage with specific calls or tell the agent to check the current status of a repository before starting work.
019dd0fe-56f2-723c-95f0-cce65db7e665 Here's how it actually works
The bottom line is: you index the code once, and then your AI client can query it endlessly without needing manual file navigation.
First, you subscribe to the Greptile MCP and provide your API key from the developer dashboard.
Next, tell your AI agent which repositories it needs to index. You can submit a new repo or trigger a re-index on an existing one.
Once indexed, simply ask your agent any question—like 'Show me how X component connects to Y service'—and get the code answer instantly.
Who is this actually for?
Anyone who has ever stared at a massive codebase—or inherited one—and had no idea where to start. This is for the developer tired of clicking through twenty different services just to understand basic dependency flow.
Uses this to quickly find implementations or trace dependencies within unfamiliar codebases, drastically cutting down research time.
Leverages it to search for related coding patterns and understand the context behind a pull request without manually reading every file.
Gets quick, high-level answers about architectural decisions or technical debt by querying the codebase through natural language conversation.
What Changes When You Connect
Stop searching documentation. Instead, ask your agent a question like 'Where is the user profile picture uploaded?' and it will use query_codebase to point you directly to the relevant service file.
Need to know if a function uses a specific database connection? Use search_by_filepath or run a semantic search with search_codebase to find every instance of that pattern across multiple files.
Don't waste time on outdated data. If your team updated the API, just trigger a re-index using reindex_repository, and all subsequent queries will use the freshest code context.
Track your work flow better with query_with_context. Instead of asking ten separate questions, you can guide the agent through an entire architectural deep dive in one session.
When onboarding a new developer to a massive system, simply running list_repositories gives you an overview, and then you use the MCP tools to onboard them by answering their specific technical queries.
See it in action
Debugging a Cross-Service Bug
A developer finds an intermittent bug in user authentication. Instead of reading every service, they ask their agent (using query_codebase) to trace the flow from token generation through the middleware. The agent identifies that src/middleware/auth.ts is using an outdated key format and points out lines 12-48.
Onboarding a New Team Member
A new engineer joins the project. To understand the database structure, they ask their agent to find all files that import the connection module. The agent uses search_codebase and reports back on 8 different services that interact with the database.
Auditing Dependencies
A code reviewer needs to confirm how many places a specific library is called. They use search_by_filepath combined with targeted queries, limiting their search to only files in the /services directory for precise results.
Architectural Planning
An engineering manager needs an overview of the frontend's structure. They first use list_repositories to ensure the 'frontend-app' repo is indexed, then ask a high-level question using query_codebase about state management patterns.
The honest tradeoffs
Searching by keyword only
Asking your agent 'database connection' and hoping it finds the right file, even if the code uses a different module name.
Use semantic search with search_codebase to find concepts. Or, for maximum precision, first run list_repositories, then use query_with_context to guide the agent through related services.
Asking vague questions
Saying 'What's wrong with the user service?' without giving context. The agent might give a generic answer that sends you down the wrong rabbit hole.
Always narrow it down. Start by using get_file_info on the suspected file, then ask your question using query_codebase, referencing the specific component name.
Relying on local knowledge
Thinking you know where a piece of code lives because it worked before. The repository might have been refactored.
Always verify by running get_repository_status first, and if the status is old, trigger reindex_repository. Then query using query_codebase to ensure accuracy.
When It Fits, When It Doesn't
Use this MCP when your core problem is 'I don't know how this code works.' It's a knowledge retrieval tool. You should use it if you need to understand architecture, trace dependencies, or find technical debt examples by asking questions in natural language. Don't use it if your goal is writing new code (use an IDE), or running tests (use CI/CD). If you only need to know the name of a file, run list_repositories. If you suspect something changed since yesterday, always check the status first with get_repository_status before trusting any answers.
Questions you might have
How do I query codebases with Greptile MCP? +
Just ask your agent a natural language question after indexing. For example: 'Show me the flow for processing payments.' The system uses query_codebase to give you a technical walkthrough.
What if my code changes? Do I need to reindex using Greptile MCP? +
Yes. If you know the codebase has changed, always run reindex_repository. Otherwise, your answers might point to outdated logic or deprecated functions.
Can I search only in one specific file path using Greptile MCP? +
Absolutely. Use the dedicated search_by_filepath tool when you need a very precise search, limiting results to files matching that exact path.
What is the difference between searching and querying with Greptile MCP? +
Search functions (search_codebase) find relevant code snippets. Querying (query_codebase) uses those findings to provide a narrative answer or explain an architectural process.
What should I use to check the status of a repository before running `query_codebase` with Greptile MCP? +
Run get_repository_status. This tool confirms if your repository is indexed and ready for queries. If the status shows 'processing,' you'll need to wait until it reports 'completed.'
How can I monitor API consumption and rate limits when using Greptile MCP? +
Use get_greptile_usage. This tool immediately displays your current API usage metrics and remaining rate limit allowance. It helps you manage costs and prevents service interruptions during long sessions.
If I need to follow up on a previous answer, should I use `query_with_context` with Greptile MCP? +
Yes, that's the right way. query_with_context maintains the conversation history within your session. This allows your agent to remember details from earlier questions when answering follow-ups.
What is the proper way to completely remove a repository from my indexed sources using Greptile MCP? +
You must run delete_repository. This tool permanently removes an entire project's data and index from your connected environment. Use it when you no longer need the code in the codebase understanding.
Can I ask natural language questions about my codebase? +
Yes! The query_codebase tool sends a natural language question along with repository references and returns AI-generated answers with specific code references (file paths and line numbers). For follow-up questions, use query_with_context with the session ID from the previous response to maintain conversation continuity.
Do I need to index my repository before querying it? +
Yes. Use index_repository with the remote host (github or gitlab), repository path (owner/repo), and branch name. Check indexing progress with get_repository_status. Once indexed, you can query and search the repository. Use reindex_repository to refresh the index after significant code changes.
Can I search for specific code patterns across my repositories? +
Yes. The search_codebase tool performs semantic search across your indexed repositories to find relevant files and functions. For targeted results, use search_by_filepath to narrow the search to a specific file path. Use get_file_info to retrieve indexed metadata for any file.
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