Knot

License: MIT Rust knot MCP server

knot is a high-performance codebase indexer that extracts structural and semantic information from source code, enabling AI agents to understand, analyze, and navigate large code repositories. Currently supports Java, Kotlin (v0.7.4+), TypeScript, JavaScript/Node.js, Rust (v0.8.x), Python (v0.9.3), HTML, and CSS/SCSS with full cross-language linking, with planned support for Groovy and C/C++.

The indexer automatically builds:

• Vector Search Database (Qdrant) — semantic understanding via embeddings
• Graph Database (Neo4j) — architectural relationships via call graphs

This dual-database approach powers both:

• MCP (Model Context Protocol) Server — Exposes three tools to any LLM client (Claude, Gemini, ChatGPT, Cursor, etc.)
• CLI Tool (v0.8.0+) — Standalone knot command for terminal and scripting environments

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✨ Key Features

🔍 Code Intelligence Tools

• search_hybrid_context: Semantic + structural search. Find code by meaning, class name, method signature, docstrings, or comments. Returns full context including dependencies.
• find_callers: Reverse dependency lookup. Identify dead code, perform impact analysis, or understand the full call chain of any function/method. When multiple entities share the same name (e.g., find_nearest_entity_by_line in different files), results are automatically grouped by target showing which specific entity each caller references.
• explore_file: File anatomy inspection. Quickly see all classes, interfaces, methods, and functions in a file with signatures and documentation.

🏗️ Multi-Language Support

• Java: Full AST extraction with package awareness
• Kotlin (v0.7.4+): Complete support for Kotlin codebases with classes, interfaces, objects, companion objects, functions, methods, and properties. Fully compatible with tree-sitter-kotlin-ng grammar.
• TypeScript/TSX/CTS: Complete support for modern JavaScript/TypeScript codebases, including CommonJS TypeScript files
• JavaScript/Node.js (v0.7.4+): Vanilla JS, Node.js, and module systems (.js, .mjs, .cjs, .jsx)
• Hybrid Web Ecosystem (v0.6.5): Cross-language linking between JavaScript, HTML, and CSS for full-stack SPA analysis
• HTML (v0.6.3+): Custom elements (Web Components, Angular), id and class attribute indexing for cross-language CSS search
• JSX/TSX Attributes (v0.6.3+): Extracts id and className from React components for unified HTML/CSS discovery
• CSS/SCSS (v0.6.4+): Stylesheet indexing with class/ID selector extraction and variable tracking (CSS/SCSS variables, mixins, functions)
• Rust (v0.8.11): Struct, enum, union, trait, function, method, module extraction with trait implementation tracking (IMPLEMENTS relationships) and macro invocation references. NEW in v0.8.6: Type alias, constant, static, and macro definition extraction with full docstring and signature support. NEW in v0.8.7: Enhanced type reference detection inside macros (vec![], println!(), assert!(), etc.) with intelligent string literal filtering and comprehensive edge case handling. NEW in v0.8.11: O(N) nested macro traversal optimization for large Rust codebases with deeply nested token_tree nodes.
• Python (v0.9.3): Full Python extraction with class, function, method support, constants, module-level imports, ValueReference tracking for keyword arguments, class inheritance (EXTENDS), decorator extraction (@property, @staticmethod, @route(...), @dataclass), generic type hints (List[str], Optional[Dict], *args/**kwargs), Py2/Py3 exception syntax compatibility, and self.method() resolution with inherited method walking. Captures class_definition, function_definition (including async via optional async modifier), lambda assignments, and distinguishes methods from functions via parent context detection.
• C/C++ (Planned v0.11.x): Pointer relationships and macro analysis
• Groovy (Planned v0.10.x): Gradle build scripts and Jenkins pipeline indexing

📚 Rich Comment Extraction

• Captures docstrings (JavaDoc, JSDoc) preceding declarations
• Extracts inline comments within method/function bodies
• Respects nesting boundaries (class comments don't capture method comments)
• Intelligently aggregates comment blocks

📊 Dual-Database Architecture

• Qdrant: Vector search for semantic code understanding
• Neo4j: Graph relationships for structural navigation

🚀 High Performance

• Parallel Streaming Pipeline: Overlaps CPU-bound embedding with I/O-bound ingestion via MPSC channels (v0.5.0+)
• Incremental Indexing: Uses SHA-256 hashes to skip unchanged files
• Real-time Watch Mode: Automatically re-indexes changed files in seconds via --watch
• CPU Parallelism: AST extraction via Rayon
• Scalable: Configurable batch processing and constant memory footprint (~2GB) regardless of repository size

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🛠️ Installation

Prerequisites

Component	Version	Notes
Docker	20.10+	For running Qdrant and Neo4j
qdrant	1.x	Vector database (docker)
neo4j	5.x	Graph database (docker)

Option A: Pre-compiled Binaries (macOS & Modern Linux)

Go to the Releases page and download the native executable for your platform.

Install knot binaries (CLI, MCP server, and indexer):

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/raultov/knot/releases/latest/download/knot-installer.sh | sh

Download agent-skills guides separately (optional):

curl -sO https://raw.githubusercontent.com/raultov/knot/master/.knot-agent.md && curl -fsSL https://raw.githubusercontent.com/raultov/knot/master/.knot-agent-skills.tar.gz | tar -xz

The first command installs the knot binary to your PATH. The second (optional) downloads the agent skill index (.knot-agent.md) and extracts comprehensive guides for using knot CLI with AI agents and code analysis tools.

Linux Requirements:

• Full install (knot-indexer + CLI + MCP): glibc 2.38+
  • Ubuntu 24.04 LTS or later
  • Debian 13 (Trixie) or later
  • Fedora 39+ / RHEL 10+
  • Arch Linux (rolling release)
• Lightweight clients-only (knot CLI + MCP server, no indexing): glibc 2.35+ (even older systems like Debian 12 Bookworm work fine)

For older Linux distributions or Windows, see the Lightweight Clients section below or use Docker (Option B).

Option B: Docker (Universal Compatibility)

Docker images provide universal compatibility for any Linux distribution and Windows.

Full Install (All Binaries: knot-indexer, knot CLI, knot-mcp)

Build the image:

docker build -t knot:latest . --network=host

Run the indexer:

# Use --network host to connect to databases running on your host machine
docker run --rm \
  -v /path/to/your/repo:/workspace \
  -e KNOT_REPO_PATH=/workspace \
  -e KNOT_NEO4J_PASSWORD=your-password \
  --network host \
  knot:latest \
  knot-indexer

Run the CLI tool:

docker run --rm \
  -v /path/to/your/repo:/workspace \
  -e KNOT_REPO_PATH=/workspace \
  -e KNOT_NEO4J_PASSWORD=your-password \
  --network host \
  knot:latest \
  knot search "user login flow"

Run the MCP server:

docker run --rm \
  -e KNOT_REPO_PATH=/workspace \
  -e KNOT_NEO4J_PASSWORD=your-password \
  --network host \
  knot:latest \
  knot-mcp

Note: Uses Debian Trixie (glibc 2.38+) and includes ONNX Runtime for full functionality.

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Lightweight Clients (Only knot CLI + knot-mcp, No Indexer)

For older systems (Debian 12 Bookworm, Ubuntu 22.04) or production deployments that only need to query existing indexes without indexing new code:

Build the lightweight image:

docker build -t knot:clients -f Dockerfile.clients . --network=host

Image size: ~100MB (vs ~160MB for full install)

Run the CLI tool (query existing index):

docker run --rm \
  --network host \
  knot:clients \
  knot callers "MyClass"

Run the MCP server:

docker run --rm \
  --network host \
  knot:clients \
  knot-mcp

Available tools in lightweight mode:

• ✅ knot search (structural only, no semantic search)
• ✅ knot callers (reverse dependency lookup)
• ✅ knot explore (file structure inspection)
• ❌ Semantic search requires the full install

Note: Uses Debian Bookworm (glibc 2.35+) and excludes ONNX Runtime, making it compatible with older Linux distributions.

Option C: Install via Cargo

cargo install --git https://github.com/raultov/knot

Option D: Build from Source

Full Install (All Binaries):

1. Start infrastructure with Docker:

docker compose up -d

2. Clone and build:

git clone https://github.com/raultov/knot
cd knot
cargo build --release

3. Configure:

cp .env.example .env
$EDITOR .env  # Set KNOT_REPO_PATH and Neo4j credentials

4. Index a codebase:

./target/release/knot-indexer

5. Query via CLI:

./target/release/knot search "your query"

Option E: Lightweight Clients (No Indexing)

For older Linux distributions (e.g. Debian 12 Bookworm, Ubuntu 22.04) or production deployments where you only need the CLI and MCP server (not the indexer), compile without the embedding dependencies:

Build lightweight clients:

cargo build --release --no-default-features --features only-clients

This produces only knot and knot-mcp binaries (~8-10 MB each), excluding the 30+ MB of ONNX Runtime dependencies.

Available tools in lightweight mode:

• ✅ find_callers: Reverse dependency lookup (graph search). Automatically groups results by target when multiple entities share the same name.
• ✅ explore_file: File structure inspection
• ❌ search_hybrid_context: Semantic search (requires embeddings, not available in this mode)

Use case: Query an existing Qdrant + Neo4j index that was built elsewhere, without needing the indexer on your machine.

Docker alternative (for lightweight mode):

docker build -t knot:clients-only -f Dockerfile -f - . << 'EOF'
FROM rust:1.90-slim-bookworm AS builder
WORKDIR /build
COPY . .
RUN cargo build --release --no-default-features --features only-clients
FROM debian:bookworm-slim
COPY --from=builder /build/target/release/knot* /usr/local/bin/
CMD ["knot-mcp"]
EOF

6. Start the MCP server:

./target/release/knot-mcp

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📖 Usage

📥 Quick Downloads

Download knot binaries (CLI + MCP server):

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/raultov/knot/releases/latest/download/knot-installer.sh | sh

Download agent-skills documentation (index + all guides):

curl -sO https://raw.githubusercontent.com/raultov/knot/master/.knot-agent.md && curl -fsSL https://raw.githubusercontent.com/raultov/knot/master/.knot-agent-skills.tar.gz | tar -xz

📖 Agent-Skills Guides

Comprehensive documentation for using knot tools. The download above extracts:

• search.md — Semantic code discovery guide with examples
• callers.md — Reverse dependency lookup with critical usage rules
• explore.md — File anatomy inspection guide
• workflows.md — Common patterns and best practices

For quick reference without downloading, see .knot-agent.md.

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Using the CLI (v0.8.0+)

The knot CLI provides the same capabilities as the MCP server via command-line commands, making it ideal for:

• Terminal-only environments
• Bash scripting and automation
• CI/CD pipelines
• Direct integration with other tools

Three main commands:

knot search — Semantic Code Search

knot search "user authentication" --max-results 10 --repo my-app

Find code entities by meaning, class names, docstrings, or comments.

knot callers — Reverse Dependency Lookup

knot callers "LoginService" --repo my-app

Find all code that references a specific entity (dead code detection, impact analysis, call chains). When multiple entities share the same name in different files, results are automatically grouped by target with file locations and signatures.

knot explore — File Structure Inspection

knot explore "src/services/auth.ts" --repo my-app

List all classes, methods, functions in a file with signatures and documentation.

For detailed CLI usage guide, see .knot-agent.md — a machine-readable skill that teaches LLMs how to use knot CLI for autonomous code analysis.

Indexing a Codebase

Incremental Indexing (Default, v0.4.3+)

# First run: indexes all files
knot-indexer --repo-path /path/to/your/repo --neo4j-password secret

# Subsequent runs: only re-indexes changed files (fast!)
knot-indexer --repo-path /path/to/your/repo --neo4j-password secret

# NEW: Real-time Watch mode (v0.5.2+)
knot-indexer --watch --repo-path /path/to/your/repo --neo4j-password secret

How it works:

• Tracks file content via SHA-256 hashes in .knot/index_state.json
• Automatically detects: modified, added, and deleted files
• Only re-parses and re-embeds changed files
• Preserves graph relationships to unchanged files
• Processes entities in memory-efficient 512-entity chunks

Performance:

• Initial index (3800 files): ~60 minutes on standard hardware
• Incremental update (3 files changed): ~5-10 seconds
• Memory usage: Constant ~2GB regardless of repository size

Full Re-Index (Clean Mode)

# Force complete re-index (deletes all existing data)
knot-indexer --clean --repo-path /path/to/your/repo --neo4j-password secret

Use --clean when:

• You want to rebuild the entire index from scratch
• You've changed Tree-sitter queries or embedding models
• Troubleshooting indexing issues

Running E2E Integration Tests

To ensure indexer stability, run the E2E integration test suite:

# Run all language E2E tests (Java, TS, JS, HTML, CSS, Kotlin, Rust)
./tests/run_e2e.sh

# Run only Kotlin E2E tests
./tests/run_kotlin_e2e.sh

# Run only Rust E2E tests
./tests/run_rust_e2e.sh

See tests/KOTLIN_E2E_TESTS.md for detailed coverage and troubleshooting.

Using the MCP Server

The MCP server exposes three tools to any compatible AI client:

Tool 1: search_hybrid_context

Find code by meaning or keywords

Query: "How is user authentication implemented?"
Result: All auth-related code, signatures, docstrings, and dependencies

Capabilities:

• Semantic search by functionality
• Class/method/function name lookup
• Docstring and inline comment search
• Architectural pattern discovery
• Full dependency context

Tool 2: find_callers

Find who calls a specific function

Query: "Find callers of getCurrentTimeInSeconds"
Result: All code that invokes this function + file locations

Advanced: Search by Signature (NEW in v0.7.4)

# Find by full signature (Java)
echo '{"method":"tools/call","params":{"name":"find_callers","arguments":{"entity_name":"registerUser(String"}}}' | knot-mcp

# Find by parameter type (Kotlin)
echo '{"method":"tools/call","params":{"name":"find_callers","arguments":{"entity_name":"findById(Int"}}}' | knot-mcp

# Find by type annotation (TypeScript)
echo '{"method":"tools/call","params":{"name":"find_callers","arguments":{"entity_name":"(EventData"}}}' | knot-mcp

Use Cases:

• Dead Code Detection: Zero callers = unused code
• Impact Analysis: "What breaks if I modify this?"
• Refactoring Safety: Find all references before removing

Tool 3: explore_file

Understand file structure

Query: "What's in BrowserService.ts?"
Result: All classes, methods, and functions with signatures and docs

Use Cases:

• Quick file navigation
• Module structure overview
• Finding all methods in a class without reading line-by-line

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🔗 MCP Client Configuration

Supported Clients

knot works with any MCP-compatible AI client:

• ✅ Claude Desktop (Anthropic)
• ✅ Gemini CLI (Google)
• ✅ ChatGPT CLI / GPT (OpenAI)
• ✅ Cursor (AI IDE)
• ✅ Any standard MCP client

Configuration Examples

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "knot": {
      "command": "/absolute/path/to/knot/target/release/knot-mcp",
      "env": {
        "KNOT_REPO_PATH": "/path/to/indexed/repo",
        "KNOT_QDRANT_URL": "http://localhost:6334",
        "KNOT_NEO4J_URI": "bolt://localhost:7687",
        "KNOT_NEO4J_USER": "neo4j",
        "KNOT_NEO4J_PASSWORD": "your-password"
      }
    }
  }
}

Gemini CLI

{
  "mcpServers": {
    "knot": {
      "command": "/absolute/path/to/knot/target/release/knot-mcp",
      "env": {
        "KNOT_REPO_PATH": "/path/to/indexed/repo",
        "KNOT_QDRANT_URL": "http://localhost:6334",
        "KNOT_NEO4J_URI": "bolt://localhost:7687",
        "KNOT_NEO4J_USER": "neo4j",
        "KNOT_NEO4J_PASSWORD": "your-password"
      }
    }
  }
}

ChatGPT / GPT CLI

Similar JSON configuration in your client's MCP configuration file.

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⚙️ Configuration Reference

All options can be set via environment variables (.env) or CLI flags. Environment variables take precedence.

.env Variable	CLI Flag	Default	Description
KNOT_REPO_PATH	--repo-path	(required)	Root directory of the repository to index
KNOT_REPO_NAME	--repo-name	(auto-detected)	Repository name for multi-repo isolation (auto-detected from last path component)
KNOT_QDRANT_URL	--qdrant-url	http://localhost:6334	Qdrant server URL
KNOT_QDRANT_COLLECTION	--qdrant-collection	knot_entities	Qdrant collection name
KNOT_NEO4J_URI	--neo4j-uri	bolt://localhost:7687	Neo4j Bolt URI
KNOT_NEO4J_USER	--neo4j-user	neo4j	Neo4j username
KNOT_NEO4J_PASSWORD	--neo4j-password	(required)	Neo4j password
KNOT_EMBED_DIM	--embed-dim	384	Embedding vector dimension
KNOT_BATCH_SIZE	--batch-size	64	Entities per batch
KNOT_CLEAN	--clean	false	Force full re-index (delete all existing data)
KNOT_CUSTOM_CA_CERTS	--custom-ca-certs	(none)	Path to CA certificate bundle for corporate SSL proxies
RUST_LOG	(env only)	info	Log level: trace, debug, info, warn, error

---

🎨 Custom Tree-sitter Queries

The built-in extraction queries (queries/java.scm, queries/typescript.scm) can be overridden without recompiling:

KNOT_CUSTOM_QUERIES_PATH=/path/to/my/queries ./target/release/knot-indexer

Place java.scm and/or typescript.scm in your custom directory. Missing files fall back to built-in defaults.

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🔐 Corporate SSL / CA Certificates

In restricted corporate environments with SSL-inspecting proxies, you may need to provide a custom CA certificate bundle so that knot can download the embedding model from HuggingFace.

Via environment variable:

export KNOT_CUSTOM_CA_CERTS=/etc/ssl/certs/corporate-bundle.pem
./target/release/knot-indexer --repo-path /path/to/repo --neo4j-password secret

Via CLI flag:

./target/release/knot-indexer \
  --custom-ca-certs /etc/ssl/certs/corporate-bundle.pem \
  --repo-path /path/to/repo \
  --neo4j-password secret

Via .env file:

echo "KNOT_CUSTOM_CA_CERTS=/etc/ssl/certs/corporate-bundle.pem" >> .env
./target/release/knot-indexer

This works for all three binaries: knot-indexer, knot-mcp, and knot.

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🔄 Workflow Example

Step 1: Index a Java project

./target/release/knot-indexer --repo-path /home/user/my-java-app --neo4j-password secret

Step 2: Query via CLI (Instant search)

./target/release/knot search "authentication logic"
./target/release/knot callers "UserService.login"

Step 3: Start MCP server (For AI Agents)

./target/release/knot-mcp

Step 4: Use with Claude Desktop

• Claude will list the three tools in its Tools menu
• Ask: "Search for all authentication logic"
• Ask: "Find who calls the login method"
• Ask: "Explore the structure of UserService.java"

🤖 Auto-Configuring AI Agents

knot includes a universal .prompt file in its root directory that automatically configures modern AI coding agents (Cursor, Cline, opencode, Claude, etc.) to use the knot-mcp tools correctly.

The directive explicitly instructs AI agents to prioritize:

• search_hybrid_context — for semantic code discovery (instead of grep)
• find_callers — for reverse dependency analysis (instead of finding references manually)
• explore_file — for file structure inspection (instead of reading line-by-line)

This ensures that when you ask an AI agent to analyze, refactor, or understand your code, it leverages the full power of the vector and graph databases rather than falling back to context-blind regex searches. The .prompt file is universal and tool-agnostic, working with any LLM client that reads codebase directives.

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🤝 Contributing

Contributions are welcome! Please ensure:

• All code passes cargo clippy
• Code is formatted with cargo fmt
• Changes are compatible with Rust 2024 edition

---

📜 License

This project is licensed under the MIT License. See LICENSE for details.

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🚀 Roadmap

Current Release (v0.9.3 — Python Complete & CI Fixes) ✅

• ✅ Python Extraction (Phase 1): tree-sitter-python integration with EntityKind variants for PythonClass, PythonFunction, PythonMethod
• ✅ Python Structural Extraction (Phase 2): class_definition, function_definition (top-level), lambda assignment captures
• ✅ Python References & Calls (Phase 3): Direct call (identifier()) and method call (object.method()) resolution, print_statement (Py2) support. CALLS edges in the dependency graph.
• ✅ Python Imports & Module Graph (Phase 4): import os and from django.db import models detection via TypeReference. PythonConstant extraction (UPPER_CASE identifiers). PythonModule synthetic entity for module-level orphans.
• ✅ Python ValueReferences (Phase 4.5): action=EnumAction patterns via keyword_argument detection. Creates REFERENCES edges for classes/functions used as argument values.
• ✅ Python Inheritance & Decorators (Phase 5): EXTENDS relationships from class inheritance (class Dog(Animal)). Decorator CALLS from @staticmethod, @property, @dataclass, @route(...). Decorator names exposed in explore_file output.
• ✅ Python Advanced Testing (Phase 6): Generic type hints (List[str], Optional[Dict]), *args/**kwargs parameter extraction, Py2/Py3 exception syntax compatibility, 5 additional unit tests, 4 additional E2E tests.
• ✅ Method vs Function Detection: is_inside_class_body() parent walk distinguishes methods from functions
• ✅ Signature Extraction: Parameter signatures captured for Python functions and methods
• ✅ CI Enhancements: Replaced unreliable nc -z network checks with Neo4j-specific Docker health checks (docker inspect), eliminating Connection reset by peer errors. Added 5s sleep between test suites.
• ✅ Search Stability: Fixed CLI search bug where -r filter flag was not correctly applied across Python/Rust/Kotlin searches.
• ✅ 376 unit tests | 24 Python E2E Tests | 22 Rust E2E | 10 Kotlin E2E

Previous Release (v0.8.11 — CLI UX Enhancements & Rust Performance) ✅

Previous Release (v0.8.7 — Enhanced Rust Type Reference Detection in Macros) ✅

• ✅ Macro Type Reference Extraction: Type references inside macro invocations (vec![], println!(), assert!(), format!(), etc.) are now correctly captured
• ✅ Intelligent String Filtering: Filters out false positives from string literals using quote-counting heuristics
• ✅ Comprehensive Edge Case Handling: Validates identifiers, handles nested macros, supports macro_rules! definitions
• ✅ Improved Accuracy: EntityKind references increased by +95.7% (46→90 references), now captures test function usage
• ✅ Enhanced Test Coverage: Added 4 new tests for token_tree extraction covering various macro types and edge cases

Previous Release (v0.8.6 — Rust Type Aliases, Constants, and Docstrings) ✅

• ✅ Rust Type Alias Extraction: Extracts type alias declarations with full signature (e.g., type Callback = fn(u32) -> u32)
• ✅ Rust Constant/Static Extraction: Captures const and static mut declarations with type signatures
• ✅ Rust Docstring Support: Full doc comment extraction for Rust entities (handles nested doc_comment nodes in tree-sitter-rust)
• ✅ Rich Vector Embeddings: Type signatures and documentation are now included in embeddings for better semantic search
• ✅ Improved Search Ranking: Rust entities like Callback now rank in top 5 search results when querying by name

Previous Release (v0.8.5 — Rust Module Refactoring & Clippy Fixes) ✅

• ✅ Rust Module Refactoring: Extracted Rust parsing logic into dedicated src/pipeline/parser/languages/rust.rs for better maintainability and mirroring existing language module architecture.
• ✅ Clippy Compliance: Fixed unused import (uuid::Uuid) and unnecessary mut warning in Rust module tests.
• ✅ Rust Support Complete: Phase 8 implementation fully integrated with 17 unit tests and 22 E2E test cases passing.

Previous Release (v0.8.4 — Agent-Skills Documentation Installer & Lightweight Clients) ✅

• ✅ Dry-Run Mode: MCP server can run in offline mode for quality checks on deployment platforms.
• ✅ Platform-Agnostic: Removed all platform-specific references; compatible with any deployment platform.
• ✅ Enhanced Reliability: Graceful handling of missing database connections for validation scenarios.

Earlier Release (v0.8.2 — Quality & Doc Refactor) ✅

• ✅ MCP Quality: Enhanced tool descriptions for better agent discovery and usage safety.
• ✅ Token-Efficient Docs: Modularized agent skill guide into docs/agent-skills/ for on-demand loading.
• ✅ Rust Phase 1: Infrastructure prepared for Rust 2024 integration.
• ✅ Rust Phase 2-5: Complete Rust language support including entity extraction, macro tracking, and comprehensive E2E testing (v0.8.x).

Earlier Release (v0.8.1 — CLI UX & Docker Integration) ✅

• ✅ Silenced CLI Logs: Default log level set to error for knot CLI (cleaner Markdown output).
• ✅ 100% E2E Dual-Testing: All 35 integration tests simultaneously verify both MCP and CLI.
• ✅ Docker CLI Support: Official Docker image now includes the knot binary.
• ✅ Agent Guidance: Enhanced .knot-agent.md with signature-based search warnings.

Phase 7 (v0.8.10 — CLI UX & Corporate Network Support) ✅

• ✅ Human-friendly output formatting: Colorized table output as default with per-entity-kind ANSI colors
• ✅ Interactive result navigation: Pager support via less -R -e with auto-exit at end of content
• ✅ Configurable output formats: --output flag supports table (default), json, and markdown
• ✅ Custom CA Certificates: --custom-ca-certs / KNOT_CUSTOM_CA_CERTS for corporate SSL-inspecting proxies
• ✅ O(N) Macro Traversal Optimization (v0.8.11): Substring skipping for deeply nested token_tree nodes

Phase 8 (v0.8.11 — Rust Support) ✅

• ✅ Support .rs files with tree-sitter-rust parser
• ✅ Struct, enum, union, trait, and impl block extraction
• ✅ Function, method, macro definition and invocation tracking
• ✅ Type alias, constant, static, and module extraction with signatures
• ✅ Docstring extraction for all Rust entity types
• ✅ O(N) nested macro traversal optimization for large Rust codebases
• ✅ 17 unit tests for Rust entity and reference extraction
• ✅ 22 end-to-end integration tests covering all Rust language constructs

Upcoming (v0.10.x+)

Phase 9: Groovy Support (v0.10.x)

• Support .groovy, .gradle, .jenkinsfile files
• Gradle build script entity extraction
• Jenkins pipeline stage and step tracking
• Closure and DSL method call resolution

Phase 10: C/C++ Support (v0.11.x)

• Support .c, .cpp, .h, .hpp files
• Pointer and memory relationship tracking
• Header inclusion graph analysis

Phase 11: YAML/Configuration Language Support

• HELM chart indexing (.yaml, .tpl)
• Kubernetes manifest analysis
• Template variable tracking and resolution

Long-Term Vision

• Markdown documentation indexing
• Go support
• C# support
• IDE plugins (VS Code, IntelliJ, Vim)
• Web UI for graph visualization
• Language Server Protocol (LSP) integration
• Automated Code Review tool (MCP-based)

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💬 Questions?

For issues, feature requests, or discussions, please open a GitHub issue.