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vfs

Virtual Function Signatures -- extract exported function, class, interface, and type signatures from source code with bodies stripped.

Why vfs?

AI coding agents waste tokens by grepping or reading entire files just to find a function. vfs parses source via AST and tree-sitter, returning only the signatures -- a compact "table of contents" of any codebase.

60-70% fewer tokens per search.

It works with any AI coding tool -- Cursor, Claude Code, Antigravity, Windsurf, Cline, Continue, Aider, Copilot, Zed, or your own scripts. No vendor lock-in.

How It Works

Given a Go project with thousands of lines, asking "where is the login handler?" traditionally means grepping or reading entire files. vfs gives you just the signatures:

$ vfs . -f login
internal/handlers/auth.go:23:   func HandleLogin(w http.ResponseWriter, r *http.Request)
internal/services/auth.go:10:   func ValidateToken(token string) (*Claims, error)
internal/middleware/jwt.go:45:  func RequireLogin(next http.Handler) http.Handler

Each line tells you the file, line number, and full signature -- no function bodies, no imports, no noise. You (or your AI agent) can then read only the exact lines needed.

This works across 13 languages:

$ vfs ./frontend -f auth
src/hooks/useAuth.ts:5:         export function useAuth(): AuthContext
src/components/LoginForm.tsx:12: export const LoginForm: React.FC<LoginFormProps>
src/api/client.py:28:           def authenticate(username: str, password: str) -> Token

Benchmark

Self-benchmark on this repository (pattern "Extract", 4,178 lines of source):

Read all files grep vfs
Output size 101.9 KB 13.8 KB 1.5 KB
Lines 4,178 148 15
Est. tokens 26,079 3,537 373
  • vfs saves 98.6% tokens vs reading all files (26,079 -> 373)
  • vfs saves 89.5% tokens vs grep (3,537 -> 373)

Run it yourself:

vfs bench --self                                   # self-test on vfs source
vfs bench -f HandleLogin /path/to/go-project       # benchmark on any project
vfs bench -f Login /path/to/project --show-output  # show actual output

Security & Privacy

Local-first by design. Your source code never leaves your machine.

  • Zero network access -- all parsing is local via AST and tree-sitter. No outbound connections, ever.
  • No secrets exposure -- does not read, access, or store API keys, credentials, or environment variables.
  • No data collection -- no telemetry, no analytics, no tracking.
  • No code storage -- source is parsed in memory and discarded. Only ~/.vfs/history.jsonl (scan statistics) is written.
  • Fully offline -- install once, use forever.

Supported Languages

Language Extensions Parser
Go .go go/ast
JavaScript .js, .mjs, .cjs, .jsx tree-sitter
TypeScript .ts, .mts, .cts, .tsx tree-sitter
Python .py tree-sitter
Rust .rs tree-sitter
Java .java tree-sitter
C# .cs tree-sitter
Dart .dart tree-sitter
Kotlin .kt, .kts tree-sitter
Swift .swift tree-sitter
Ruby .rb tree-sitter
HCL / Terraform .tf, .hcl tree-sitter
Dockerfile Dockerfile, Dockerfile.* line-based
Protobuf .proto line-based
SQL .sql line-based
YAML .yml, .yaml line-based

Install

Your situation Method What you need
Linux Pre-built binary Nothing
macOS / Linux / Windows Build from source Go 1.24+, C compiler
Any OS Docker Docker

Pre-built binary

Download from GitHub Releases. No Go, no C compiler needed. Each release includes SHA-256 checksums.

# Linux x86_64
curl -L https://github.com/TrNgTien/vfs/releases/latest/download/vfs-linux-amd64.tar.gz | tar xz
sudo mv vfs /usr/local/bin/

# Linux ARM64
curl -L https://github.com/TrNgTien/vfs/releases/latest/download/vfs-linux-arm64.tar.gz | tar xz
sudo mv vfs /usr/local/bin/

Build from source

Requires Go 1.24+ and a C compiler:

  • macOS: xcode-select --install
  • Linux: sudo apt install build-essential (Debian/Ubuntu) or sudo yum groupinstall "Development Tools" (Fedora/RHEL)
  • Windows: install TDM-GCC (easiest) or MSYS2 + MinGW-w64
git clone https://github.com/TrNgTien/vfs.git && cd vfs
go install ./cmd/vfs

vfs: command not found? Add Go's bin to your PATH: export PATH="$PATH:$(go env GOPATH)/bin" (macOS/Linux) or add %USERPROFILE%\go\bin to PATH (Windows).

Docker

docker build -t vfs-mcp .
docker run --rm -v $(pwd):/workspace -p 8080:8080 -p 3000:3000 vfs-mcp

# Custom ports via environment variables
docker run --rm -v $(pwd):/workspace -e VFS_PORT=9090 -e VFS_DASHBOARD_PORT=4000 -p 9090:9090 -p 4000:4000 vfs-mcp

Quick Start

# Find a function by name (case-insensitive)
vfs . -f HandleLogin

# Scan specific directories
vfs ./internal ./pkg

# List all signatures in a single file
vfs server.go

# Show token savings stats after output
vfs . -f auth --stats

# Start the MCP server + dashboard in the background
vfs up

# Start on a custom port (default: 8080)
vfs up --port 9090

# Check server status
vfs status

# Stop the server
vfs down

Open the dashboard at http://localhost:3000 to see usage statistics and token savings over time.

Run vfs --help for all commands and flags.

CLI Reference

vfs [paths...] -f <pattern>

The main command. Scans files/directories and prints exported signatures.

vfs .                          # all signatures in current directory (recursive)
vfs ./src ./lib                # scan multiple directories
vfs handler.go                 # single file
vfs . -f auth                  # filter by pattern (case-insensitive)
vfs . -f auth --stats          # show token efficiency stats after output
vfs . -f auth --no-record      # skip logging to history

Flags:

Flag Description
-f, --filter Case-insensitive substring filter on signature names
--stats Print token efficiency stats (raw vs vfs) to stderr
--no-record Skip logging this invocation to ~/.vfs/history.jsonl

vfs bench

Compare token usage: reading all files vs grep vs vfs.

vfs bench --self                              # benchmark on vfs's own source
vfs bench -f HandleLogin /path/to/project     # benchmark on any project
vfs bench -f Login /path/to/project --show-output  # also print actual output

vfs stats

Show lifetime token savings across all recorded invocations.

vfs stats            # show summary
vfs stats --reset    # clear all history

Example output:

--- vfs lifetime stats ---
Invocations:         142
Total tokens saved:  ~52,300
Total raw scanned:   2.3 MB  (48,200 lines)
Total vfs output:    89.5 KB  (1,420 lines)
Avg reduction:       72.3%
First recorded:      2025-01-15 09:30
Last recorded:       2025-03-09 14:22

vfs mcp

Start the MCP server for AI tool integration.

vfs mcp                  # stdio transport (default, for editor integration)
vfs mcp --http :8080     # HTTP transport (for Docker / remote setups)

vfs serve

Run the MCP server (HTTP) and dashboard together in the foreground.

vfs serve                                    # defaults: MCP on :8080, dashboard on :3000
vfs serve --port 9090                        # MCP on :9090
vfs serve --port 9090 --dashboard-port 4000  # both custom
vfs serve --mcp :9090 --dashboard-port 4000  # equivalent (full address form)

vfs up / vfs down / vfs status

Manage the server as a background process.

vfs up                  # start MCP + dashboard in background (default port 8080)
vfs up --port 9090      # start on custom MCP port
vfs status              # check if running, show endpoints
vfs status --port 9090  # check custom port
vfs down                # stop the background server

vfs dashboard

Run just the dashboard web UI (without MCP server).

vfs dashboard                # default port 3000
vfs dashboard --port 4000    # custom port

Setup for AI Tools

Setting up vfs requires two steps:

  1. Connect vfs -- configure MCP or make the CLI available so the agent can call vfs.
  2. Add an agent rule -- tell the agent it should call vfs before grep. Without this, the agent will still default to grep/read even if vfs is available.

Step 2 is critical. AI agents don't automatically know vfs exists. You must add a rule file that instructs the agent to use vfs for code discovery. Each tool has its own rule file format -- see Step 2: Agent Rules below.

Step 1: Connect vfs

vfs works with any AI coding tool that supports MCP (Model Context Protocol). If your tool doesn't support MCP, you can use vfs as a CLI command that the agent calls via shell.

Method How it works Best for
MCP (recommended) Agent calls vfs tools directly via MCP protocol Editors with MCP support (most modern AI editors)
CLI Agent runs vfs as a shell command Terminal-based tools, scripts, tools without MCP

Method 1: MCP Integration (recommended)

MCP lets the AI agent call vfs tools (search, extract, stats, list_languages) directly without shell access. This works even in sandboxed environments where the agent can't run arbitrary binaries.

MCP Tools

MCP Tool Description Parameters
search Find signatures matching a pattern paths (string[]), pattern (string)
extract Return all exported signatures paths (string[])
stats Lifetime usage statistics none
list_languages Supported languages and extensions none

Most tools use the same stdio JSON config. The only difference is where the file lives:

Tool MCP config location
Cursor .cursor/mcp.json (project) or ~/.cursor/mcp.json (global)
Claude Code .mcp.json (project) or claude mcp add vfs -- vfs mcp
Claude Desktop ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
Antigravity MCP settings panel, or project MCP config
Windsurf .windsurf/mcp.json (project) or global via Windsurf settings
Cline MCP config in VS Code Cline extension settings
Continue .continue/config.json under experimental.modelContextProtocolServers
Zed ~/.config/zed/settings.json under context_servers

Stdio config (Cursor, Claude Code, Claude Desktop, Antigravity, Windsurf, Cline):

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

Continue uses a different structure:

{
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "vfs",
          "args": ["mcp"]
        }
      }
    ]
  }
}

Zed uses a different structure:

{
  "context_servers": {
    "vfs": {
      "command": {
        "path": "vfs",
        "args": ["mcp"]
      }
    }
  }
}

HTTP config (for Docker, remote setups, or any tool that supports HTTP-based MCP):

vfs up                  # starts MCP on :8080 and dashboard on :3000
vfs up --port 9090      # starts MCP on :9090 and dashboard on :3000
{
  "mcpServers": {
    "vfs": {
      "url": "http://localhost:8080/mcp"
    }
  }
}

If using a custom port, update the URL accordingly (e.g. http://localhost:9090/mcp).

Method 2: CLI Integration

For tools that don't support MCP (Aider, custom scripts, CI), use vfs as a shell command:

vfs . -f CreateUser
# Output: internal/services/user.go:42: func CreateUser(name string, email string) (*User, error)

vfs . -f handler | head -20

LOCATION=$(vfs . -f CreateUser | head -1)
FILE=$(echo "$LOCATION" | cut -d: -f1)
LINE=$(echo "$LOCATION" | cut -d: -f2)
echo "Found at $FILE line $LINE"

Step 2: Define an Agent Rule (required)

Installing vfs is not enough. AI agents don't automatically know vfs exists. Without an explicit rule, the agent will still default to grep and reading entire files -- wasting the tokens vfs is designed to save.

AGENTS.md in this repo is documentation that explains how vfs works. It is not a rule that forces agents to use vfs. You need to create a rule file in your own project.

You must create a rule file in your project that instructs the agent: "use vfs before grep for code discovery." This repo ships a production-ready rule at .cursor/rules/vfs-agent-search.mdc -- you can reuse it directly or adapt it for your tool.

Each AI tool has its own rule system:

Tool Rule file location How to reuse vfs-agent-search.mdc
Cursor .cursor/rules/vfs.mdc Copy directly: cp vfs-agent-search.mdc yourproject/.cursor/rules/
Claude Code CLAUDE.md Copy the content into your CLAUDE.md (strip the YAML frontmatter)
Antigravity GEMINI.md Copy the content into your GEMINI.md (strip the YAML frontmatter). Also reads AGENTS.md.
Windsurf .windsurf/rules/vfs.md Copy as-is: cp vfs-agent-search.mdc yourproject/.windsurf/rules/vfs.md
Cline .clinerules Copy the content into your .clinerules (strip the YAML frontmatter)
Continue .continue/rules/vfs.md Copy as-is: cp vfs-agent-search.mdc yourproject/.continue/rules/vfs.md
Aider .aider.conventions.md Copy the content into your .aider.conventions.md (strip the YAML frontmatter)

What to put in the rule file

The core instruction is the same regardless of tool. Create the rule file for your tool (see table above) and add this content:

# vfs: Use AST-based search before grep

When looking for function definitions, method signatures, class names, or type
declarations, you MUST use vfs before grep or reading entire files.

## How to call vfs

MCP (preferred -- works in sandboxed editors):
  search(paths: ["."], pattern: "functionName")

CLI (fallback -- if MCP is not available):
  vfs . -f functionName

## Workflow

1. Call vfs search with the name you're looking for.
2. vfs returns file paths and line numbers.
3. Read ONLY the specific lines returned -- not the whole file.

## When to skip vfs and use grep directly

- Searching inside function bodies (string literals, error messages, config keys)
- Searching non-code files (JSON, CSS, .env, markdown)
- You already know the exact file and line number
- vfs returned no results for your query

## Why this matters

vfs parses source via AST and returns only signatures (bodies stripped).
This saves 60-70% tokens compared to grep. Do not skip this step.

Example: setting up for Cursor

mkdir -p .cursor/rules

Then create .cursor/rules/vfs.mdc with the rule content above. This repo includes a complete, production-ready Cursor rule at .cursor/rules/vfs-agent-search.mdc that you can copy directly:

cp /path/to/vfs/.cursor/rules/vfs-agent-search.mdc .cursor/rules/

Example: setting up for Antigravity

Create GEMINI.md in your project root with the rule content above. Antigravity reads GEMINI.md as its native config. It also reads AGENTS.md for general agent instructions, but the rule that forces vfs usage should go in GEMINI.md.

Example: setting up for Claude Code

Create or append to CLAUDE.md in your project root with the rule content above. Claude Code reads this file at the start of every session.

Example: setting up for Windsurf

mkdir -p .windsurf/rules

Then create .windsurf/rules/vfs.md with the rule content above.

Why this matters

Without the rule file, here's what happens:

You: "Where is the login handler?"

❌ Without rule:  Agent runs `grep -r "HandleLogin" .` → reads 200 lines → 3,500 tokens
✅ With rule:     Agent calls vfs search("HandleLogin") → reads 23 lines → 370 tokens

The rule file is what turns vfs from "installed but ignored" into "actively saving tokens on every search."

The VERSION file at the repo root contains the current semver.

License

MIT

About

Reduce AI agent token usage by 98% via Virtual Function Signatures. MCP server for Cursor & Claude Desktop. Supports Go, TypeScript, Python, Rust, Java, Swift, Kotlin, C# and more.

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