Layer

Smarter context for faster coding agents.

A small, fast MCP server written in Rust that helps AI coding agents understand your codebase instead of guessing at it.

Why Layer · Benchmarks · How it works · Languages · Tools · Install · CLI

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Why Layer

If you use coding agents like Claude Code, Cursor, Windsurf, Codex or OpenCode on real projects, you have probably seen these problems:

1. The agent can't see the big picture. On a repo with thousands of files, agents fall back to grep and dump whole files into the prompt. They read 1,500 line files, burn 100k+ tokens just scanning, lose the thread and go in circles.

2. Fixes turn into guessing games. The agent edits something, breaks a caller it never read, then spends five turns guessing how to repair it.

3. New projects get hallucinated APIs. When starting from scratch, agents invent package functions or get stuck in broken config.

Layer sits between your agent and your code and talks over the standard Model Context Protocol. Instead of raw file dumps, the agent gets symbol outlines, exact function slices, impact graphs and compile checks in milliseconds.

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Benchmarks

Both tests used the same model, the same prompt and the same starting repo state. No cherry picked prompts. Independent testing is very welcome.

Real bug: NSA Ghidra

Fixing a PowerPC 64 bit width mismatch in the decompiler (Issue #9886) using gpt-6-luna.

Metric	Without Layer	With Layer (v1.3)	Change
Time taken	7m 57s	2m 50s	2.8x faster
Input tokens	101,391	37,477	-63.0%
Output tokens	14,474	5,731	-60.4%
Reasoning tokens	7,313	2,666	-63.5%
Cache read tokens	5,880,320	754,432	-87.2%
Total tokens	5,996,185	797,640	-86.7%
Cost (USD)	$0.08	$0.01	-87.5%

Without Layer, the agent spent nearly eight minutes on noisy text searches across thousands of C++ and Sleigh files. With Layer, it found the exact instruction semantics in under three minutes.

See the raw agent responses

With Layer (2m 50s, 797,640 tokens, $0.01)

“I made a Sleigh semantics change in ppc_instructions.sinc (Ghidra/Processors/PowerPC/data/languages/ppc_instructions.sinc:3888): tw, its trap alias, and twi now pass the low 32-bit register values to trapWord. For twi, the immediate is sign-extended to 32 bits. td and tdi are unchanged. This addresses the architectural width mismatch: these word-trap instructions compare 32-bit words even when GPRs are 64 bits. I left RuleSubCommute::applyOp unchanged; its INT_ADD case already avoids commuting a spacebase input. The issue’s attached reproducer and expected output were not present in the checkout, so I could not establish the reported function-level failure or prove the fix resolves it.

Validation is incomplete. The Layer navigation calls did not return. The Gradle wrapper could not download Gradle because network access was unavailable, and the Sleigh launcher reports that this checkout has not been compiled. I therefore could not compile the modified Sleigh, run processor or decompiler tests, or perform a before-and-after decompilation. No regression test was added, and I can’t claim the fix passes. The change still needs Sleigh compilation and the requested reproducer or equivalent automated regression before it is validated.”

Without Layer (7m 57s, 5,996,185 tokens, $0.08)

“The fix is incomplete; I can’t verify it against the reported failure.

I added a RuleSubCommute::applyOp path in ruleaction.cc (Ghidra/Features/Decompiler/src/decompile/cpp/ruleaction.cc:4589). When a low-word SUBPIECE reads an INT_ADD result that has other readers, it now rewrites that SUBPIECE as a narrow add and leaves the original wide add intact. This targets the root cause described in issue #9886 (NationalSecurityAgency/ghidra#9886), but I have not added a regression test or demonstrated before-and-after decompiler output.

In this checkout, the PowerPC tw and twi semantics already pass 4-byte operands to trapWord in ppc_instructions.sinc. That matches word-trap behavior, so I left Sleigh unchanged. The issue’s alternative semantics patch appears unnecessary for this source; td and tdi remain separate full-width operations.

Validation: make -j2 ghidra_opt succeeded. make -j2 test stopped because the environment lacks bfd.h. I couldn’t fetch the issue’s attached ELF because network DNS is unavailable, and the installed Clang has no PowerPC target, so I couldn’t construct an equivalent binary here. The repository also has no .git metadata, which prevents a Git diff or status check.

The source change is therefore not yet supported by the required reproducible regression. I can’t claim the reported bug is fixed based on the validation available here.”

Build from scratch: 3D gravity simulator

A full 3D N Body orbital gravity and collision simulator (React, TypeScript, Three.js, Tailwind CSS) built from an empty folder.

Metric	Without Layer	With Layer	Change
Total tokens	1,002,538	567,206	-43.4%
Input tokens	59,985	43,844	-26.9%
Output tokens	12,505	7,778	-37.8%
Reasoning tokens	1,613	557	-65.5%
Cache read tokens	930,048	515,584	-44.6%
Cost (USD)	$0.02	$0.01	-50%
Build failures	2 (a syntax error and an unused import)	0	Caught in under 15ms
Frame rate	43 FPS, unstable third body	61 FPS, stable orbit	Better result

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How it works

Layer does four main things.

1. Instant syntax checks (under 15ms)

Every edit is checked in memory using compiled Tree sitter syntax trees. Layer validates grammar, balanced brackets, strings and imports without waiting for a slow compiler to start (tsc alone takes 400 to 800ms to boot).

2. Safe multi file refactors

When an agent changes a shared interface like src/types.ts, Layer separates errors in the file you edited from errors in files that simply haven't been updated yet. Good interface edits are kept, and the agent gets a pending_downstream_updates checklist instead of a rollback.

3. Full compiler checks and an error circuit breaker

Layer plugs into your real toolchain (tsc, cargo check, pyright, go vet, clang++, dotnet, javac, mvn) and sorts every diagnostic into new, persistent or resolved. If the same error survives two or more turns, the circuit breaker steps in and feeds the agent verified type signatures and definitions so it stops guessing.

4. Task aware context

Instead of reading whole files, Layer ranks code using BM25F scoring, Personalized PageRank and test pairing. It returns small slices with exact line numbers, saving three to five wasted grep and read turns per task.

Built to scale (v1.3)

• No lock contention. Indexing runs in the background and swaps in atomically, so agent queries never wait on a big scan.
• Fast cold starts. A seed scanner capped at 250ms handles the first query on a fresh repo.
• Constant time lookups. Hash indexes replace full repo scans, so 10,000+ symbols are searched in under 40ms with a hard 450ms deadline.
• Do no harm by default. auto_rollback_on_cascade is off, so modern syntax (Python 3.12, TypeScript 5.x) never triggers a false rollback.

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Supported languages

Layer supports 25+ languages through compiled Tree sitter grammars and its own ULBA engine (Universal Lexical and Block Automaton).

Language	Symbols and graph	Instant syntax check	Package inspection	Scaffolding	Compiler check
TypeScript and JavaScript	✅ Tree sitter	✅	✅ node_modules, .d.ts	✅	✅ tsc
Rust	✅ Tree sitter	✅	✅ Cargo crates	✅	✅ cargo check
Python	✅ Tree sitter	✅	✅ .venv, stubs	✅	✅ pyright
Go	✅ Tree sitter	✅	✅ go.mod	✅	✅ go vet
C and C++	✅ Tree sitter	✅	✅ #include headers	✅	✅ AST fallback
Java	✅ ULBA	✅	✅ Spring Boot	✅	✅ javac
C#	✅ ULBA	✅	✅ ASP.NET	✅	✅ dotnet
Swift	✅ ULBA	✅	✅ SPM	✅	✅ AST fallback
Kotlin	✅ ULBA	✅	✅ Gradle	✅	✅ AST fallback
Ruby, PHP, Scala, Dart, Zig, Elixir, Lua, SQL, Shell	✅ ULBA	✅	✅ Manifests	✅	✅ AST fallback

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Tools

Layer exposes 23 tools over standard MCP JSON-RPC.

Navigate and understand

Tool	What it does
find_symbol	Finds a declaration with its file, line range, signature and docstring
get_symbol_body	Returns just one function, method or struct without the surrounding file
get_file_skeleton	A compact outline of any file in about 350 tokens
get_callers	Lists every caller of a function, grouped by enclosing symbol
find_references	Finds all usages, ignoring comments and strings
get_impact_radius	Shows callers, callees and confidence before you refactor
get_lines	Reads an exact range of lines with zero overhead
get_tree	Shows the repo layout with file counts and symbol density
search	Fast fuzzy search across the codebase
prepare_task_context	Builds a compact context packet for a given prompt
set_workspace	Switches the active workspace root

Start new projects

Tool	What it does
bootstrap_project	Creates a fresh project (react-ts, rust-bin, rust-lib, python, go, cpp, swift, kotlin, csharp, java) without overwriting anything
scaffold_module	Generates a multi file module with typed interfaces and stubs in one turn
scaffold_template	Creates a starter template such as rust-cli
inspect_package_api	Reads real package declarations before the agent imports them
init_harness	Writes AGENTS.md, CLAUDE.md or .cursorrules so agents prefer Layer tools

Edit safely

Tool	What it does
write_file	Writes a file with a snapshot, syntax check and circuit breaker. Supports verify_mode of fast, full or none
edit_file	Applies exact block edits with a syntax check and automatic rollback
rename_symbol	Renames a symbol across the whole project in one go, rolling back everything if a syntax error appears
rollback_last_edit	Undoes the latest change, including multi file edits

Verify and measure

Tool	What it does
verify_project	Runs checks and compares against the last run (new_errors, persistent_errors, resolved_errors)
verify_impacted_tests	Runs only the tests affected by your changes
get_session_stats	Shows tool calls, latency, estimated tokens saved and interventions

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Install

Quick install

No Rust toolchain or C compiler needed. The script finds your platform, downloads the latest binary and adds it to your PATH.

macOS and Linux

curl -fsSL https://raw.githubusercontent.com/junaidmirr/layer/master/install.sh | sh

Windows (PowerShell)

irm https://raw.githubusercontent.com/junaidmirr/layer/master/install.ps1 | iex

Download a binary

Grab a build from GitHub Releases.

Platform	Architecture	Format
Windows	x86_64	.zip
macOS	Apple Silicon	.tar.gz
macOS	Intel	.tar.gz
Linux	x86_64	.tar.gz

Docker

docker run -i --rm -v $(pwd):/workspace ghcr.io/junaidmirr/layer:latest mcp

Build from source

git clone https://github.com/junaidmirr/layer.git
cd layer
cargo install --path . --force
layer --help

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Connect to your agent

Add Layer as an MCP server in your agent's config.

Claude Code (~/.claude/claude_desktop_config.json or your project config) and Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "layer": {
      "command": "layer",
      "args": ["mcp"]
    }
  }
}

Windsurf, OpenCode or any MCP client

{
  "name": "layer",
  "command": "layer",
  "args": ["mcp"],
  "transport": "stdio"
}

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CLI usage

You can also use Layer straight from the terminal.

layer mcp                        # start the MCP server
layer skeleton src/graph.rs      # outline a file
layer find VectorGraph           # locate a symbol
layer body resolve_module_import_path   # print one function
layer impact inspect_package_api # see what an edit could affect
layer watch                      # run the file watcher
layer stats                      # live session dashboard
layer stats --reset              # reset the counters
layer init                       # create steering files for your agent
layer init codex                 # steering file for Codex
layer init claude                # steering file for Claude Code
layer init --alias               # enable `codex --layer` and `claude --layer`

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Development

cargo test                                              # 101 tests across 10 suites
cargo clippy --all-targets --all-features -- -D warnings # lint
cargo fmt --check                                       # formatting

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License

Layer is released under the MIT License.