Search Tools MCP Server
# ๐ Search Tools MCP Server
> โก An intelligent Model Context Protocol (MCP) server that supercharges code analysis with advanced search capabilities and dependency mapping
## ๐ Overview
The **Search Tools MCP Server** is a powerful toolkit that combines traditional code search with intelligent analysis algorithms. It leverages the **CodeRank** algorithm (inspired by PageRank) to identify the most critical modules in your codebase and provides sophisticated search capabilities that go beyond simple text matching.
## ๐ฏ Key Features
### ๐ **Smart Search Capabilities**
- **Contextual Keyword Search**: Ripgrep-powered search with configurable context lines
- **Symbol Discovery**: Extract and analyze functions, classes, methods, and modules
- **Usage Tracking**: Find where symbols are used across your codebase
- **Priority-Ranked Results**: Search results ranked by code importance
### ๐ง **Intelligence & Analysis**
- **CodeRank Algorithm**: Identify the most critical modules using network analysis
- **Dependency Mapping**: Trace complex dependency chains and impact analysis
- **Hotspot Detection**: Find code areas that are both highly connected and frequently used
- **Refactoring Impact**: Analyze the potential impact of code changes
### ๐จ **Advanced Filtering**
- Symbol type filtering (functions, methods, classes)
- File inclusion/exclusion patterns
- External module dependency tracking
- Markdown documentation analysis
## ๐ ๏ธ Installation
### Prerequisites
- Python 3.13+
- `uv` package manager
- `kit` CLI tool (for symbol analysis)
- `ripgrep` (for fast text search)
### Setup
```bash
# Clone the repository
git clone <repository-url>
cd search-tools
# Install dependencies
uv sync
```
## โ๏ธ Configuration
### Adding to Cursor/Windsurf
Add the following configuration to your `mcp.json` file:
```json
{
"mcpServers": {
"search-tools": {
"command": "/path/to/uv",
"args": [
"run",
"--directory",
"/path/to/search-tools",
"main.py"
]
}
}
}
```
**For macOS users with Homebrew:**
```json
{
"mcpServers": {
"search-tools": {
"command": "/Users/yourusername/.local/bin/uv",
"args": [
"run",
"--directory",
"/path/to/your/search-tools/directory",
"main.py"
]
}
}
}
```
### To add to claude code:
```bash
claude mcp add-json search-tools '{"type":"stdio","command":"/Users/yourusername/.local/bin/uv","args":[ "run", "--directory", "/path/to/your/search-tools/directory", "main.py"]}'
```
### ๐ Finding Your Paths
To find the correct paths for your system:
```bash
# Find uv location
which uv
# Get absolute path to search-tools directory
pwd # (run this from the search-tools directory)
```
## ๐ Available Tools
### ๐ `contextual_keyword_search`
Search for keywords with configurable context lines around matches.
**Parameters:**
- `keyword`: Search term (case insensitive)
- `working_directory`: Absolute path to search directory
- `num_context_lines`: Lines of context (default: 2)
### ๐๏ธ `get_repo_symbols`
Extract symbols (functions, classes, methods) from your codebase.
**Parameters:**
- `repo`: Repository path
- `working_directory`: Command execution directory
- `keep_types`: Filter by symbol types
- `file_must_contain/file_must_not_contain`: File filtering
### ๐ `get_symbol_usages`
Find where specific symbols are used throughout your codebase.
**Parameters:**
- `repo`: Repository path
- `symbol_name_or_substring`: Symbol to search for
- `working_directory`: Command execution directory
- `symbol_type`: Optional type filter
### ๐ฏ `coderank_analysis`
Analyze repository importance using the CodeRank algorithm.
**Parameters:**
- `repo_path`: Repository to analyze
- `external_modules`: Comma-separated external dependencies
- `top_n`: Number of top modules to return (default: 10)
- `analyze_markdown`: Include markdown files
- `output_format`: "summary", "detailed", or "json"
### ๐ฅ `find_code_hotspots`
Identify critical code areas combining connectivity and usage frequency.
**Parameters:**
- `repo_path`: Repository path
- `working_directory`: Command execution directory
- `min_connections`: Minimum import connections (default: 5)
- `include_external`: Include external dependencies
- `top_n`: Number of hotspots to return (default: 20)
### ๐ `trace_dependency_impact`
Trace dependency chains and analyze refactoring impact.
**Parameters:**
- `repo_path`: Repository path
- `target_module`: Module to analyze
- `working_directory`: Command execution directory
- `analysis_type`: "dependency", "refactoring", or "both"
- `max_depth`: Maximum trace depth (default: 3)
- `change_type`: "modify", "split", "merge", or "remove"
### ๐ช `smart_code_search`
Enhanced search combining ripgrep with CodeRank prioritization.
**Parameters:**
- `keyword`: Search term (supports regex)
- `repo_path`: Repository path
- `working_directory`: Command execution directory
- `rank_results`: Sort by module importance
- `context_lines`: Context lines around matches (default: 3)
- `max_results`: Maximum results to return (default: 20)
## ๐งช Development & Testing
### Running the Server
```bash
# Development mode
uv run mcp dev main.py
# Testing with MCP Inspector
npx @modelcontextprotocol/inspector python main.py
```
### ๐ง Dependencies
- **mcp[cli]**: Model Context Protocol framework
- **cased-kit**: Symbol analysis toolkit
- **networkx**: Graph analysis for CodeRank algorithm
## ๐จ Algorithm Details
### CodeRank Algorithm
The CodeRank algorithm treats your codebase as a directed graph where:
- **Nodes**: Python modules, classes, functions, methods
- **Edges**: Import relationships and dependencies
- **Weights**: Different weights for internal vs external dependencies
This creates a ranking system that identifies the most "central" and important parts of your codebase, similar to how PageRank identifies important web pages.
## ๐ก Use Cases
- **๐ Code Exploration**: Quickly understand large codebases
- **๐๏ธ Refactoring Planning**: Identify high-impact areas before changes
- **๐ Documentation**: Find the most important modules to document first
- **๐ Bug Investigation**: Focus on critical code paths
- **๐ฅ Code Review**: Prioritize review efforts on important modules
## ๐ค Contributing
Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.
## ๐ License
This project is open source. Please check the license file for details.
---
<div align="center">
**๐ฎ Powered by the CodeRank Algorithm & Model Context Protocol**
*Making code search intelligent, one repository at a time*
</div>
TDQS
Scored across 21 tools
Each tool targets a specific analysis task, but several overlap (e.g., smart_code_search vs contextual_keyword_search; find_code_hotspots vs coderank_analysis) requiring careful reading of descriptions. The distinct focus areas keep them mostly separable.
Tool names mix various verb prefixes (get_, find_, trace_, analyze_, discover_, map_, identify_) and some names are noun-led (coderank_analysis, contributor_impact_analysis). No consistent pattern emerges, making the surface harder to predict.
21 tools is in the borderline heavy range per the rubric. Each tool covers a distinct analysis scenario, so it is not excessive, but the number is still large and could overwhelm an agent choosing which tool to invoke.
The set covers a wide array of code analysis needsโsearch, symbols, dependencies, data flow, error handling, performance, testingโand includes several composite tools. Minor gaps like dead code detection don't significantly detract from the overall surface.