surgical-context-mcp — Surgical AST Code Context & Knowledge Graph for AI Coding Agents

Stop feeding 3,000-line files into your LLM context window. surgical-context-mcp is a zero-LLM-cost, embedded SQLite + Tree-sitter Code Knowledge Graph and MCP Server that gives AI coding agents (Claude Code, Cursor, Windsurf, Cline, Claude Desktop) the exact surgical context they need to edit, refactor, and navigate codebases safely — cutting token consumption by 70–95% without sacrificing code quality.

⚡ Quick Start (30 seconds)

# 1. Install (or: pipx install / pip install surgical-context-mcp)
uv tool install surgical-context-mcp

# 2. Register with Claude Code
claude mcp add code-graph -- code-graph serve

# 3. Index your project + install the token-saving guard hook
code-graph install-hooks /path/to/your/project

That's it — your agent now calls get_edit_context instead of reading whole files. Using Cursor, Windsurf, Cline or Claude Desktop? See Step 1. Also listed on the official MCP Registry as io.github.erayerturk/surgical-context-mcp.

Related MCP server: engram-serve

Table of Contents

1. Quick Start (30 seconds)

2. Why Use surgical-context-mcp?

3. How Much Does It Save & When Should You Use It?

4. Supported Languages & Ecosystem Resolvers

5. How It Works (Architecture)

6. Installation

7. How to Use (Step-by-Step Setup Guide)

8. Real Output Examples

9. MCP Tools Reference

10. CLI Reference

11. Recommended CLAUDE.md / .cursorrules Configuration

Why Use surgical-context-mcp?

The 3 Core Problems with AI Coding Agents Today

1. Full-File Reads Burn Hundreds of Thousands of Tokens: When you ask an AI agent (or when it spawns subagents) to fix a bug or add a feature, its default instinct is to run Grep and then Read entire 1,500–3,000 line files into the context window. Because every subsequent tool call re-sends the entire conversation history (cache reads), a single large file read early in a session compounds into 100k–300k wasted tokens.

2. Narrow Line-Range Reads Cause Hallucinations & Code Duplication: If you force an agent to read only lines 240–260 of a file to save tokens, the agent becomes blind to:

   • Class attributes, self.x fields, @onready / @export variables, constants, and struct / interface definitions at the top of the file.

   • Helper functions that already exist in the same file (causing the LLM to write duplicate code).

   • How callers actually pass arguments to the function (±5 lines around call sites).

   • The file's coding style (indentation, typing conventions, error handling idioms).

3. Pure Static AST Graphs Miss Dynamic Dispatch & Scene/Template Wiring: Standard call graphs only see static foo() calls. If a function is invoked via getattr(obj, "foo") in Python, call("foo") or a .tscn signal connection in Godot, SendMessage("foo") in Unity/C#, or referenced in a .vue/.html/.proto file, a naive graph reports "0 callers — safe to delete", breaking production code.

How surgical-context-mcp Solves All Three

• get_edit_context(symbol) — One Call Before Any Edit: Instead of reading a 2,500-line file, the agent makes one MCP call (get_edit_context) and receives a strictly budgeted (≤ 4,000 tokens) surgical package containing:

  1. Target Function Full Body (with exact line numbers for Edit)

  2. File Skeleton (all imports, interface/struct/enum blocks, class fields, constants — without other function bodies)

  3. Used Members & Direct Callees (definitions of class fields used by the target function + full bodies of short same-file helpers ≤ 25 lines)

  4. Direct Callers (d=1, ±5 Line Windows) (exact call-site context without dumping 200-line caller functions)

  5. Other Function Signatures in the File (so the agent reuses existing helpers instead of duplicating them)

  6. Style Hints & Coverage Warnings (flags dynamic string calls, .tscn connections, templates, and protocol references)

• check_rename_or_delete(symbol) — One Call Before Any Refactor: Checks AST callers (exact/scoped), inheritance overrides across parent/child classes, .tscn/template connections, signal emit/connect sites, and workspace-wide dynamic string references.

• Zero LLM Indexing Cost & Sub-Second Speed: Built on native C Tree-sitter grammars and embedded SQLite (WAL mode). Indexes ~600 files in < 0.6 seconds using 0 API tokens, and automatically refreshes modified files (mtime + SHA-1) on every query.

• Enforced via Claude Code PreToolUse Hook: Includes a built-in hook guard (code-graph install-hooks) that blocks unbounded Read calls on files > 300 lines and returns the file's AST symbol table (Lstart-end: signature), ensuring both main agents and subagents stay on the surgical path.

How Much Does It Save & When Should You Use It?

1. Token & Cost Economics (Single-Turn vs. Multi-Turn Compounding Savings)

In agentic coding tools (Claude Code, Cursor, Cline), every tool call re-sends the entire conversation history up to that point. When an agent reads a 2,500-line file (~22,000 tokens) on Turn 2 of a 12-turn coding task, those 22,000 tokens are billed on Turn 2, Turn 3, Turn 4 ... all the way to Turn 12 (22,000 × 10 = 220,000 cumulative context tokens).

By replacing full-file reads with get_edit_context (~2,500–3,500 tokens), surgical-context-mcp prevents context bloat from compounding across turns.

Benchmark Across Common Developer Tasks

(Measured on a 593-file multi-language repository: 2,607-line Godot client controller + Go backend + JS web dashboard)

2. When Does surgical-context-mcp Provide the Biggest Gains?

High-Impact Scenarios (80%–97% Savings & Higher Code Accuracy)

• Large or Monolithic Source Files (300 to 5,000+ lines): Game controllers (main.gd, PlayerController.cs), large backend services (server.go, views.py), or complex React/TypeScript modules where you only need to edit 1–2 functions without losing sight of class fields, types, and existing helpers.

• Cross-File Refactoring & Signature Changes: When changing parameters on a widely used function or method, check_rename_or_delete and get_edit_context show every caller's exact ±5 line call window without loading entire caller files into memory.

• Event-Driven, Signal & Scene-Wired Architectures: Codebases using Godot signals & .tscn connections, Node/JS EventEmitter / bus.on, Python @on_event decorators, or C# events — where standard Grep cannot trace execution flow from emitter to listener.

• Polyglot & Full-Stack Monorepos: Projects combining Go + TypeScript, Python + React, or Godot + Go + JS, where naive text search mixes up identically named methods (connect, refresh, update, send) across languages.

• Long Coding Sessions & Subagent Workflows: Keeps your main conversation window lean so Claude Code / Cursor doesn't hit the context limit or degrade in reasoning quality after 15 minutes of work.

When Are Savings Smaller? (Full Transparency)

• Small Files (< 300 lines): If a file is only 150 lines long, slicing it into a skeleton + body is unnecessary overhead. surgical-context-mcp detects this automatically: for files < 300 lines, get_edit_context returns the full numbered file directly.

• Pure Test-Runner / Compiler Output Loops: If a subagent spends 20 minutes running go test ./... or pytest in a loop and reading 5,000 lines of stack traces from terminal stdout, those tokens come from bash test output rather than source-file reads.

• Greenfield Scaffolding (0 existing files): When generating a brand-new project from scratch in an empty directory, there is no existing codebase graph to query until the first files are written.

Supported Languages & Ecosystem Resolvers

surgical-context-mcp strictly isolates language families (so a method in JavaScript or Go never collides with a method of the same name in Python or GDScript) and parses real ecosystem configuration files to resolve cross-file imports with exact confidence:

How It Works (Architecture)

flowchart LR
    subgraph Workspace["Project Workspace"]
        Code["Source Files\n(.py, .ts, .tsx, .go, .rs, .cs, .java, .gd, .tscn)"]
        Configs["Ecosystem Configs\n(tsconfig.json, go.mod, project.godot)"]
    end

    subgraph Engine["surgical-context-mcp (Local, Zero-LLM)"]
        Scanner["Incremental Scanner\n(mtime + SHA-1 + .gitignore)"]
        Parser["Tree-sitter Polyglot AST\n+ Config Resolver"]
        DB[(".code_graph.sqlite\nNodes & Scoped Edges")]
        Scanner --> Parser --> DB
        Configs --> Parser
    end

    subgraph AgentTools["MCP Tools & Claude Hooks"]
        Map["get_architecture_map\n(God Nodes & Clusters)"]
        EditCtx["get_edit_context\n(Skeleton + Body + Callers)"]
        Safety["check_rename_or_delete\n(Callers + Overrides + Dynamic Refs)"]
        Hook["PreToolUse Hook Guard\n(Blocks >300L Read)"]
    end

    Code --> Scanner
    DB --> Map
    DB --> EditCtx
    DB --> Safety
    DB --> Hook

Confidence Levels on Every Graph Edge

Every relationship in .code_graph.sqlite is tagged with one of three confidence levels:

• exact: Resolved within the same file/class, via an explicit file import (tsconfig.json @/ alias, go.mod package, Python relative import, Godot project.godot Autoload, .tscn script), or direct super/inheritance chain.

• scoped: Unambiguous symbol resolved via imported modules or unique within the same language family.

• name_only: Ambiguous name match with multiple candidates. Excluded from primary output and summarized separately under ### Uncertain (name_only, max 5) so it never pollutes context.

Installation

Prerequisites

• Python 3.10 – 3.13 (Python 3.12 recommended)

• macOS, Linux, or Windows

Quick Install (recommended)

Published on PyPI and the official MCP Registry.

# with uv (fastest)
uv tool install surgical-context-mcp

# or with pipx
pipx install surgical-context-mcp

# or plain pip
pip install surgical-context-mcp

This puts two commands on your PATH: code-graph (CLI + MCP server) and surgical-context-mcp (MCP server shortcut).

code-graph --help

Prefer a persistent install (uv tool / pipx / pip) over one-off uvx runs: install-hooks writes the absolute path of the code-graph binary into your Claude Code settings, so it must not live in a temporary cache.

Install from Source

git clone https://github.com/erayerturk/surgical-context-mcp.git
cd surgical-context-mcp
python3.12 -m venv .venv
source .venv/bin/activate
pip install -e .

How to Use (Step-by-Step Setup Guide)

Step 1: Register the MCP Server with Your AI Coding Tool

Option A: Claude Code (CLI)

Run this once to register code-graph globally in Claude Code:

claude mcp add code-graph -- code-graph serve

Option B: Cursor / Windsurf / Claude Desktop (.mcp.json or claude_desktop_config.json)

Add the following to your .mcp.json (project root) or global MCP settings file (if your editor doesn't inherit your shell PATH, use the absolute path from which code-graph):

{
  "mcpServers": {
    "code-graph": {
      "command": "code-graph",
      "args": ["serve"]
    }
  }
}

Step 2: Initialize Your Target Project & Install the Guard Hook

Run install-hooks on any project repository you want to work on:

code-graph install-hooks /path/to/your/project

What this command does automatically:

1. Builds .code_graph.sqlite in /path/to/your/project/.code_graph.sqlite (respecting .gitignore, nested .gitignore, .codegraphignore, and skipping node_modules, .venv, .git worktrees, build artifacts, etc.).

2. Generates GRAPH_REPORT.md in your project root — a compact architectural summary (~60 lines) listing your Subsystem Clusters, God Nodes (most connected production classes/functions), Autoloads, and Top Event/Signal Bus Channels.

3. Installs the Claude Code PreToolUse Hook into /path/to/your/project/.claude/settings.local.json:

   • Whenever Claude Code (or any subagent it spawns) attempts an unbounded Read on a code file larger than 300 lines, the hook intercepts the call, blocks the 3,000-line read, and returns a compact list of all functions/classes and their line numbers (Lstart-end: signature) in that file, instructing the agent to call get_edit_context or Read(offset, limit).

   • Whenever the agent runs Grep for a symbol name, the hook appends the exact AST definition coordinates from .code_graph.sqlite.

Tip: Add .code_graph.sqlite* to your project's .gitignore so the local SQLite database is not committed to git.

Step 3: Add the Workflow Rules to CLAUDE.md or .cursorrules

Copy the Recommended CLAUDE.md Configuration below into your project's CLAUDE.md (or .cursorrules).

Step 4: How Your AI Agent Uses It in Practice

Once configured, you don't need to run manual commands — your AI agent will automatically follow this workflow:

1. Exploring the Project: The agent reads GRAPH_REPORT.md or calls get_architecture_map() to immediately see the project's directory clusters and hub classes.

2. Editing a Function or Method: Before touching any symbol, the agent calls:

   get_edit_context(symbol="computeFinalScore", project_path="/path/to/project")

   It gets the exact function body, the file skeleton (interface/struct/var declarations), used members, short helper bodies, and caller windows in one shot, and immediately applies Edit without reading the whole file.

3. Renaming or Deleting Code: Before removing a function or changing its parameters, the agent calls:

   check_rename_or_delete(symbol="computeFinalScore", project_path="/path/to/project")

   It verifies all callers, inheritance overrides, scene/template links, and dynamic string references.

Real Output Examples

Example 1: get_edit_context on a Large File

Command:

code-graph edit-context build_edit_context -p /path/to/surgical-context-mcp

Output (abbreviated):

index: fresh
## Edit Context: `build_edit_context` (`src/code_graph/context_builder.py:462-649`, 188L / file 1264L)

### Style Hint
- indent: 4 spaces
- conventions: type annotations (`-> ReturnType`); `_private` helpers

### 1. Target Function Body (`src/code_graph/context_builder.py:462-649`, exact)
```python
 462: def build_edit_context(
 463:     db: CodeGraphDB,
 464:     symbol: str,
 465:     budget_tokens: int = 4000,
 466:     refreshed_files: int = 0,
 467: ) -> dict[str, Any]:
 ...

2. File Skeleton (src/code_graph/context_builder.py — imports, signals, vars, consts)

   3: from __future__ import annotations
   5: import os
   6: import re
   7: from pathlib import Path
  10: from .db import CodeGraphDB
  12: from .models import GraphNode
  22: SMALL_EDIT_FILE_LINES = 300
  23: SHORT_CALLEE_MAX_LINES = 25

3. Used Members & Direct Callees

Referenced File Members:

• 22: SMALL_EDIT_FILE_LINES = 300

• 23: SHORT_CALLEE_MAX_LINES = 25 Same-File Callees:

• _read_file_lines [exact] (src/code_graph/context_builder.py:26-30):

  26: def _read_file_lines(full_path: Path) -> list[str]:
  27:     try:
  28:         return full_path.read_text(encoding="utf-8", errors="replace").splitlines()
  29:     except OSError:
  30:         return []

External Callees:

• CodeGraphDB.find_symbol_nodes [scoped] (src/code_graph/db.py:694-781) — def find_symbol_nodes(self, symbol: str) -> list[GraphNode]:

4. Direct Callers (d=1, ±5 Line Windows)

• _cmd_edit_context [scoped, calls @ src/code_graph/cli.py:130] (window src/code_graph/cli.py:125-135):

 128:         stats = indexer.index(force=False)
 129:         refreshed = stats.files_indexed + stats.files_removed
 130:         ctx = build_edit_context(
 131:             db=indexer.db,
 132:             symbol=args.symbol,

5. Other Function Signatures in src/code_graph/context_builder.py

• L419-455 (37L): def format_coverage_warnings_section(

• L652-797 (146L): def format_edit_context_markdown(ctx: dict[str, Any]) -> str:

• L826-988 (163L): def build_rename_or_delete_check(

Coverage Warnings

• No unresolved textual references found.

---

## MCP Tools Reference

All MCP tools have concise `≤ 2`-sentence docstrings to minimize system-prompt overhead in every session, and all tools auto-refresh modified files before running.

| MCP Tool | Parameters | Description |
| :--- | :--- | :--- |
| **`get_edit_context`** | `symbol`, `budget_tokens=4000`, `project_path=None` | **Call before editing any symbol.** Returns file skeleton, full target body, used members, short same-file callees, `±5L` caller windows, other function signatures, style hints, and coverage warnings within `budget_tokens` (or full file if `<300` lines). |
| **`get_architecture_map`** | `top_n=15`, `project_path=None` | **Call to understand repo structure.** Returns Subsystem Clusters (directories), God Nodes (highest-centrality production classes/functions), Godot Autoloads, and Top Event/Signal Hubs. |
| **`check_rename_or_delete`** | `symbol`, `project_path=None` | **Call before deleting, renaming, or changing a signature.** Returns all `exact`/`scoped` call sites, inheritance overrides, `.tscn`/template connections, signal `emit`/`connect` sites, and dynamic string matches. |
| **`get_symbol_impact`** | `symbol`, `depth=2`, `max_lines=300`, `max_tokens=800`, `include_uncertain=False`, `project_path=None` | Returns upstream callers, downstream callees, event bus connections, and surgical `±5L` read windows up to `depth` hops. |
| **`find_flow`** | `from_symbol`, `to_symbol`, `max_depth=8`, `project_path=None` | Finds the shortest call/event path between two symbols across functions, methods, and signals/events. |
| **`search_symbols`** | `query=""`, `kind=None`, `limit=30`, `project_path=None` | Searches AST nodes by name/substring and optional `kind` (`function`, `method`, `class`, `event`). Supports `file.py::Class.method` selectors. |
| **`reindex_project`** | `project_path=None`, `force=False` | Runs incremental `mtime` + `SHA-1` indexing (or full re-index if `force=True`) and updates `.code_graph.sqlite`. |

---

## CLI Reference

You can also use `code-graph` directly from your terminal:

```bash
# 1. Index a project (fast incremental mtime + SHA-1 scan)
code-graph index /path/to/project

# 2. Force a full re-index from scratch
code-graph index /path/to/project --force

# 3. Generate GRAPH_REPORT.md + install Claude Code PreToolUse guard hooks
code-graph install-hooks /path/to/project

# 4. Get surgical edit context for a symbol (<= 4000 tokens)
code-graph edit-context MyClass.my_method -p /path/to/project --budget 4000

# 5. Check if a symbol is safe to rename, change signature, or delete
code-graph check my_method -p /path/to/project

# 6. Inspect upstream callers & downstream callees (depth 1..5)
code-graph impact my_method --depth 2 -p /path/to/project

# 7. Find shortest call/event path between two symbols
code-graph flow handle_request send_notification -p /path/to/project

# 8. Search symbols by substring and kind (function | method | class | event)
code-graph search auth --kind method -p /path/to/project

# 9. Print or write the Architecture Map (GRAPH_REPORT.md)
code-graph report /path/to/project --write

# 10. Run diagnostics (DB size, language breakdown, top hubs, stale file check)
code-graph doctor /path/to/project

# 11. Watch directory for live changes and auto-update .code_graph.sqlite
code-graph watch /path/to/project

# 12. Start the MCP server over stdio
code-graph serve

Recommended CLAUDE.md / .cursorrules Configuration

Paste this section into your project's CLAUDE.md, AGENTS.md, or .cursorrules so main agents and subagents follow the surgical workflow automatically:

## Code Graph (`code-graph` MCP) — Mandatory Token-Saving Workflow

1. **Architecture First (`GRAPH_REPORT.md` / `get_architecture_map`):**
   - At the start of a task, check `GRAPH_REPORT.md` (or call `get_architecture_map`) to identify subsystem clusters, God Nodes, and event channels before browsing directories.
2. **Before Editing Any Symbol (`get_edit_context`):**
   - Always call `get_edit_context(symbol="<name>", budget_tokens=4000)` BEFORE modifying a function, method, or class.
   - It provides: (1) File skeleton (imports, types, structs/interfaces, class vars, constants), (2) Full target function body with line numbers, (3) Used member definitions & short same-file callee bodies, (4) Direct caller `±5L` windows, (5) Other function signatures in the file, (6) Style hints, and (7) `Coverage Warnings`.
   - Do **NOT** read the full file after calling `get_edit_context`; apply your `Edit` directly using the returned line numbers.
3. **Before Deleting, Renaming, or Changing Signatures (`check_rename_or_delete`):**
   - Always call `check_rename_or_delete(symbol="<name>")` before removing a symbol or changing its signature to verify all `exact`/`scoped` callers, inheritance overrides, scene/template connections, and dynamic string references (`getattr`, `call("x")`).
4. **Targeted Impact & Flow Queries:**
   - Use `get_symbol_impact(symbol="<name>", depth=2)` to inspect upstream/downstream call chains.
   - Use `find_flow(from_symbol="<A>", to_symbol="<B>")` to trace execution or event paths between two symbols.
   - If a symbol name is ambiguous, pass `path/to/file.ext::SymbolName` or `ClassName.method`.
5. **No Unbounded Reads on Large Files (>300 Lines):**
   - Never call `Read` on a source file >300 lines without `offset` and `limit` (enforced by the `code-graph` `PreToolUse` hook).

Custom Ignore Rules (.codegraphignore)

By default, surgical-context-mcp automatically respects:

• Root and nested .gitignore files

• Nested git worktrees / submodules (any subdirectory containing its own .git)

• Built-in ignored directories (.git, .claude, .aider*, .venv, venv, node_modules, dist, build, target, vendor, .next, .godot, Library, Temp, Binaries, etc.)

If you want to exclude additional directories or generated files from the graph without adding them to .gitignore, create a .codegraphignore file in your project root using standard .gitignore syntax:

# .codegraphignore
*.pb.go
*.generated.ts
third_party/
fixtures/large_dumps/

Running the Test Suite

.venv/bin/python tests/test_end_to_end.py

Acknowledgements

The GRAPH_REPORT.md / "God Nodes" architecture-summary idea was inspired by graphify. surgical-context-mcp is an independent implementation with a different focus: per-edit surgical context (get_edit_context), refactor safety checks (check_rename_or_delete), and a PreToolUse hook that keeps agents off full-file reads.

Contributing

Issues and PRs are welcome — especially new language resolvers and real-world token benchmarks. If surgical-context-mcp saves you tokens, a ⭐ helps others find it.

License

MIT © Eray Erturk. Free for personal and commercial use.