Nuanced MCP Server
An MCP server that enables LLMs to understand and analyze code structure through function call graphs, allowing AI assistants to explore relationships between functions and analyze dependencies in Python repositories.
MattMorgis
Tools
initialize_graph
Initialize a code graph for the given repository path. Args: repo_path: Path to the repository to analyze Returns: Success message with information about the initialized graph
switch_repository
Switch to a different initialized repository. Args: repo_path: Path to the repository to switch to Returns: Success message or error
list_repositories
List all initialized repositories. Returns: List of initialized repositories
get_function_call_graph
Get the call graph for a specific function. Args: file_path: Path to the file containing the function function_name: Name of the function to analyze repo_path: Optional repository path (uses active repository if not specified) Returns: Information about the function's call graph
analyze_dependencies
Find all module or file dependencies in the codebase. Identifies all function dependencies for a file or module in the active repository. This identifies all modules that depend on the specified module or file. Args: file_path: Path to a specific file to analyze dependencies for module_name: Name of a module to analyze dependencies for (e.g., 'auth' will match 'app.auth', 'auth.users', etc.) Returns: A list of all functions and files that depend on the specified module
analyze_change_impact
Analyze the impact of changing a specific function. This tool performs a comprehensive impact analysis to help understand what would be affected if you modify the specified function. Args: file_path: Path to the file containing the function function_name: Name of the function to analyze Returns: A detailed analysis of the potential impact of changing the function
README
Nuanced MCP Server
A Model Context Protocol (MCP) server that provides call graph analysis capabilities to LLMs through the nuanced library.
Overview
This MCP server enables LLMs to understand code structure by accessing function call graphs through standardized tools and resources. It allows AI assistants to:
- Initialize call graphs for Python repos
- Explore function call relationships
- Analyze dependencies between functions
- Provide more contextually aware code assistance
API
Tools
-
initialize_graph
- Initialize a code graph for the given repository path
- Input:
repo_path
(string)
-
switch_repository
- Switch to a different initialized repository
- Input:
repo_path
(string)
-
list_repositories
- List all initialized repositories
- No inputs required
-
get_function_call_graph
- Get the call graph for a specific function
- Inputs:
file_path
(string)function_name
(string)repo_path
(string, optional) - uses active repository if not specified
-
analyze_dependencies
- Find all module or file dependencies in the codebase
- Inputs (at least one required):
file_path
(string, optional)module_name
(string, optional)
-
analyze_change_impact
- Analyze the impact of changing a specific function
- Inputs:
file_path
(string)function_name
(string)
Resources
-
graph://summary
- Get a summary of the currently loaded code graph
- No parameters required
-
graph://repo/{repo_path}/summary
- Get a summary of a specific repository's code graph
- Parameters:
repo_path
(string) - Path to the repository
-
graph://function/{file_path}/{function_name}
- Get detailed information about a specific function
- Parameters:
file_path
(string) - Path to the file containing the functionfunction_name
(string) - Name of the function to analyze
Prompts
-
analyze_function
- Create a prompt to analyze a function with its call graph
- Parameters:
file_path
(string) - Path to the file containing the functionfunction_name
(string) - Name of the function to analyze
-
impact_analysis
- Create a prompt to analyze the impact of changing a function
- Parameters:
file_path
(string) - Path to the file containing the functionfunction_name
(string) - Name of the function to analyze
-
analyze_dependencies_prompt
- Create a prompt to analyze dependencies of a file or module
- Parameters (at least one required):
file_path
(string, optional) - Path to the file to analyzemodule_name
(string, optional) - Name of the module to analyze
Usage with Claude Desktop
Add this to your claude_desktop_config.json
UV
{
"mcpServers": {
"nuanced": {
"command": "uv",
"args": [
"--directory",
"/path/to/nuanced-mcp",
"run",
"nuanced_mcp_server.py"
]
}
}
}
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