NicolasVautrin/CallGraph
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The CallGraph MCP Server is designed for analyzing the call graph of Axelor projects, enabling intelligent queries about method and class usage, impact analysis, and dependency navigation.
CallGraph MCP Server
MCP server for analyzing call graphs in Axelor projects using ASM bytecode analysis.
What is this?
A Model Context Protocol (MCP) server that lets Claude Code intelligently query your project's call graph:
- Find where methods/classes are used
- Analyze change impact
- Navigate dependencies
- Trace call chains across Java bytecode
Key Features:
- ASM bytecode analysis instead of source parsing (100% accurate)
- SQLite relational database instead of vector database
- Gradle dependency discovery for automatic Axelor package detection
- Smart caching with SHA256-based invalidation
Quick Start
1. Install dependencies
pip install -r requirements.txt
2. Configure Claude Code
Add to your .claude/mcp.json or claude_desktop_config.json:
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"sqlite-callgraph": {
"command": "uvx",
"args": ["mcp-server-sqlite", "--db-path", "C:/path/to/project/.callgraph.db"]
}
}
}
3. Extract your project's call graph
cd /path/to/your/axelor/project
# Incremental extraction (default, uses cache)
python /path/to/CallGraph/run_asm_extraction.py .
# Full reset (first time or after schema changes)
python /path/to/CallGraph/run_asm_extraction.py . --init
# Limit extraction for testing (requires --init)
python /path/to/CallGraph/run_asm_extraction.py . --init --limit 100
This creates .callgraph.db in your project directory.
Modes:
- Default (Incremental): Smart caching - only re-extracts modified packages
- --init: Full reset - drops all tables and rebuilds from scratch
4. Restart Claude Desktop
The MCP server provides direct SQLite access to the call graph database.
Architecture Overview
Project (.callgraph.db SQLite database)
↓ Extraction via
run_asm_extraction.py
├─> GradleDependencyManager # Auto-discover Axelor deps
└─> ASMExtractor # Python client
↓ REST API
ASMAnalysisService (Java) # Port 8766
↓ ASM ClassVisitor
Bytecode Analysis (.class files)
↓ Storage
SQLite Tables:
├─> symbol_index # FQN → URI → package
├─> nodes # classes, methods
└─> edges # calls, inheritance, member_of
Components
1. run_asm_extraction.py
CLI entry point for the extraction pipeline.
Usage:
# Incremental extraction (default, uses cache)
python run_asm_extraction.py /path/to/project
# Full reset (first time or after schema changes)
python run_asm_extraction.py /path/to/project --init
# Limit extraction for testing (requires --init to avoid partial data)
python run_asm_extraction.py /path/to/project --init --limit 100
Modes:
- Incremental (default): Uses SHA256 caching - only re-extracts modified packages
- --init: Full reset - drops all tables and rebuilds from scratch
Process (always runs both steps):
- Package Discovery via
GradleDependencyManager(Gradle) - Symbol Indexing via
ASMExtractor.build_symbol_index()(FQN → URI) - Call Graph Extraction via
ASMExtractor.extract()(Nodes + Edges)
2. GradleDependencyManager.py
Discovers and manages Axelor dependencies via Gradle.
Features:
- Query Gradle for runtime JAR dependencies (Axelor only)
- Extract JARs to
axelor-repos/cache directory - Provide package metadata (group, artifact, version, jar, sources, classes)
Cache Structure:
axelor-repos/
├── axelor-core-7.2.6/
│ ├── classes/ # .class files from JAR
│ └── sources/ # .java files from sources JAR
└── axelor-base-8.2.9/
├── classes/
└── sources/
3. ASMExtractor.py
Python client for ASMAnalysisService with SQLite storage.
Database Schema:
-- Symbol index (FQN → URI → package)
CREATE TABLE symbol_index (
fqn TEXT PRIMARY KEY,
uri TEXT NOT NULL,
package TEXT NOT NULL,
line INTEGER -- Line number (methods only)
);
-- Nodes (classes and methods)
CREATE TABLE nodes (
fqn TEXT PRIMARY KEY,
type TEXT NOT NULL, -- 'class', 'interface', 'enum', 'method'
package TEXT NOT NULL,
line INTEGER,
visibility TEXT, -- 'public', 'private', 'protected', 'package'
has_override BOOLEAN, -- TRUE if @Override annotation present
is_transactional BOOLEAN -- TRUE if @Transactional annotation present
);
-- Edges (relationships)
CREATE TABLE edges (
id INTEGER PRIMARY KEY AUTOINCREMENT,
from_fqn TEXT NOT NULL,
edge_type TEXT NOT NULL, -- 'call', 'inheritance', 'member_of'
to_fqn TEXT NOT NULL,
kind TEXT, -- 'invoke', 'extends', 'implements', 'argument', 'return'
from_package TEXT NOT NULL,
to_package TEXT NOT NULL,
from_line INTEGER
);
Constructor:
ASMExtractor(db_path=".callgraph.db", service_url="http://localhost:8766", init=False)
init=True: Full reset - drops and recreates all tables (INIT mode)init=False(default): Incremental mode - creates tables if they don't exist
Key Methods:
init_database(): Full reset - drops and recreates all tables (called automatically when init=True)clean_package_data(package_name): Removes all data for a specific package (used automatically in incremental mode)build_symbol_index(): Builds FQN → URI mapping with automatic cache invalidationextract(): Extracts call graph from bytecode
Performance:
- Symbol indexing: ~6 minutes for 39 packages (107k symbols, 9.4k classes)
- Call graph extraction: ~40 seconds for 9,466 .class files (252 files/sec)
- Total extraction (--init): ~6-7 minutes complete project
- Incremental mode: Only re-extracts modified packages (70%+ speedup on subsequent runs)
Optimizations:
- Batch SQL queries (IN clauses): 99.95% reduction in database queries
- Before: ~650k queries → After: ~220 queries
- Insertions by batches of 5000 rows
4. ASMAnalysisService (Java)
REST service for analyzing Java bytecode using ASM.
Technology:
- ASM: Bytecode manipulation framework
- Spark Java: Lightweight HTTP framework
- Jackson: JSON serialization
Endpoints:
GET /health- Health checkPOST /index- Extract symbols (classes AND methods) with nodeType and linePOST /analyze- Extract complete call graph with metadataPOST /shutdown- Gracefully shut down the service
Extracted Metadata:
- Class modifiers (public, abstract, final, etc.)
- Method modifiers and visibility
- Annotations:
@Override,@Transactional(Spring, Jakarta, javax) - Line numbers for methods
- Inheritance relationships
- Method calls with line numbers
Running the Service:
cd Extracteurs/ASMAnalysisService
./gradlew.bat run
# Service starts on port 8766
# Logs written to asm-service.log
MCP Tools (SQLite)
Use your configured SQLite MCP server with direct SQL queries.
Query Examples
Find methods using class X as argument:
SELECT DISTINCT e.to_fqn AS method_fqn, e.from_fqn AS argument_type
FROM edges e
WHERE e.edge_type = 'member_of' AND e.kind = 'argument'
AND e.from_fqn = 'com.axelor.apps.openauction.db.Lot'
ORDER BY e.to_fqn;
Find all calls to a method:
SELECT e.from_fqn AS caller, e.to_fqn AS callee, e.from_line AS line, e.from_package
FROM edges e
WHERE e.edge_type = 'call' AND e.to_fqn LIKE '%setStatus%'
ORDER BY e.from_package;
Count symbols by package:
SELECT package, COUNT(*) as symbol_count,
SUM(CASE WHEN type = 'class' THEN 1 ELSE 0 END) as class_count,
SUM(CASE WHEN type = 'method' THEN 1 ELSE 0 END) as method_count
FROM nodes
GROUP BY package ORDER BY symbol_count DESC;
Find transactional methods:
SELECT fqn, package, line
FROM nodes
WHERE type = 'method' AND is_transactional = 1
ORDER BY package, line;
Find public methods with @Override:
SELECT fqn, package, line
FROM nodes
WHERE type = 'method' AND visibility = 'public' AND has_override = 1
ORDER BY package, line;
Comparison: JavaParser vs ASM
| Feature | JavaParser (Old) | ASM (New) |
|---|---|---|
| Input | Source code (.java) | Bytecode (.class) |
| Requires sources | Yes | No (works with JARs) |
| Analysis speed | Slower (parsing) | Faster (bytecode) |
| Accuracy | Source-level | Bytecode-level (100%) |
| Dependencies | Manual download | Gradle auto-discovery |
| Storage | ChromaDB (~150 MB) | SQLite (~50 MB) |
| Query speed | ~50ms | ~10ms |
Benefits of ASM Approach
- No source code required: Works directly with JARs from Maven/Gradle cache
- Automatic dependency discovery: Gradle integration
- 100% accurate: Bytecode analysis reflects actual compilation
- Faster: No parsing overhead
- Smaller database: Relational storage vs vector embeddings
- Better caching: SHA256-based invalidation
- Simpler queries: SQL vs vector similarity search
Performance
Extraction Times (Real Project - 39 Packages)
Full Extraction (--init): ~6-7 minutes total
- STEP 1 (Gradle discovery): ~10 seconds
- STEP 2 (Symbol indexing): ~6 minutes for 107k symbols
- STEP 3 (Call graph extraction): ~40 seconds for 9,466 .class files (252 files/sec)
Incremental Mode: Only re-extracts modified packages (70%+ speedup)
Database Stats
Real Project Example:
- Size: ~292 MB
- Symbols: 107,312 (9,466 classes + 97,846 methods)
- Edges: 319,272 (calls, inheritance, member_of)
- Packages: 39 Axelor packages
- Entities: 2,089 (22% of classes)
Database Contents:
symbol_index: 107,312 rows (~60 MB)nodes: 94,267 rows (~52 MB)edges: 319,272 rows (~180 MB)
Query Performance
- Simple queries (COUNT, SELECT WHERE): ~10ms
- Complex joins (call chains): ~50-100ms
- Full-text search (LIKE): ~20-30ms
Troubleshooting
ASMAnalysisService not starting
Solutions:
- Check Java:
java -version(need Java 11+) - Build service:
cd Extracteurs/ASMAnalysisService && gradlew.bat build - Start manually:
gradlew.bat service:run
Cache not detected
Verification:
ls axelor-repos/axelor-core-7.2.6/classes/
# Should contain .class files
FQN resolution fails or stale data
Solution:
- Full reset:
python run_asm_extraction.py /path/to/project --init - This drops all tables and rebuilds from scratch
Kill stuck Java processes
# Windows
wmic process where "name='java.exe' and CommandLine like '%ASMAnalysisService%'" delete
Development
Project Structure
CallGraph/
├── run_asm_extraction.py # CLI entry point
├── mcp_callgraph_server.py # Legacy MCP server
├── mcp.json # MCP configuration
├── ASM_ARCHITECTURE.md # Detailed architecture
├── Extracteurs/
│ ├── GradleDependencyManager.py # Gradle integration
│ ├── ASMExtractor.py # Python client
│ └── ASMAnalysisService/ # Java service
│ └── service/ # Kotlin/Gradle implementation
└── axelor-repos/ # Cached Axelor packages
File Naming Conventions
PascalCase for Python files (matches main class):
ASMExtractor.py→class ASMExtractorGradleDependencyManager.py→class GradleDependencyManager
License
MIT
Resources
- - Detailed technical architecture
- ASM Documentation
- Spark Java
- SQLite
- MCP Protocol