We’ve all had this experience with a coding agent.
You ask it to make a relatively simple change in a large codebase and the agent starts traversing files like it’s lost in a shopping mall.
It opens dozens of irrelevant files.
It reads thousands of lines it doesn’t need.
It loses context.
It wastes tokens unnecessarily.
And after a few minutes it finally finds what it was looking for.
The problem is usually not the model.
The problem is that the agent doesn’t understand the semantic structure of the codebase.
Serena tries to solve exactly that.
It’s an open source MCP server designed to give agents semantic understanding of code at the level of symbols, relationships, and architecture. Instead of navigating files line by line, the agent can reason about classes, functions, references, and dependencies directly.
The best way to describe it is:
What if your coding agent actually understood how your application is built?
What does Serena do?
Serena adds a layer of semantic analysis on top of your repository and exposes that information through MCP.
In practice, it allows an agent to answer questions like:
- Where is this function used?
- What classes implement this interface?
- What modules depend on this component?
- What’s the complete flow from this endpoint to the database?
- What symbols would be affected by this change?
All of this without having to read large amounts of unnecessary code.
Symbol-level navigation
Most current agents work primarily at the file level.
Serena works at the level of:
- classes,
- methods,
- functions,
- interfaces,
- references,
- exported symbols.
For example, instead of asking:
“Open all files related to authentication”
the agent can ask:
“Show me all references to
AuthService.”
The difference seems small.
In large repositories, it’s huge.
Real-world use cases
1. Enormous codebases
In repositories with:
- hundreds of directories,
- thousands of files,
- monolithic architectures,
agents often waste much of their tokens simply exploring.
Serena drastically reduces that exploration.
2. Complex refactors
Suppose you want to rename or modify:
PaymentService
Without semantic analysis, the agent must search for text references.
With Serena it can identify:
- direct calls,
- implementations,
- dependencies,
- related symbols.
This makes refactors considerably safer.
3. Onboarding on new projects
One of the most interesting uses.
You can ask the agent:
“Explain how the authentication system works.”
or:
“Show me the complete flow from API to persistence.”
The agent can respond using actual architectural relationships.
4. Architectural understanding
Serena lets you navigate an application as if it were a semantic graph.
For example:
- dependencies between modules,
- relationships between components,
- class hierarchies,
- call flows.
This is particularly useful when working with legacy systems.
Installation
The easiest way is to use uv.
First install Serena:
uv tool install serena-agent
It can also be run directly:
uvx serena-agent
Integration with Claude Code
Serena can be exposed as an MCP server for Claude Code.
For example:
claude mcp add serena \
-- uvx serena-agent
Once configured, Claude Code gains access to all the semantic capabilities of the server.
Useful commands
Some of the most interesting capabilities include:
Find references
find_referencing_symbols
Allows you to discover all places where a symbol is used.
Find definitions
find_symbol
Ideal for quickly locating implementations.
File overview
get_symbols_overview
Provides a structured representation of the symbols present.
Navigate dependencies
find_referenced_symbols
Allows you to explore semantic relationships between components.
Supported languages
Currently Serena supports multiple popular languages, including:
- Python
- TypeScript
- JavaScript
- Go
- Java
- Rust
- C#
- PHP
Coverage continues to grow actively.
Why does this matter?
For years we tried to solve the context problem simply by adding more tokens.
200K windows.
1 million.
2 million.
But more context doesn’t necessarily mean better understanding.
Serena represents a different approach.
Instead of making the agent read more code, it makes the agent understand the code it already has in front of it better.
And that could be much more important.
Because as codebases continue to grow, the advantage won’t belong to the agent that can read more files.
It will belong to the agent that can navigate the architecture the way an experienced developer would.