mcp_server.cpython-310.pyc•13.4 kB
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MCP Server Interface
Implements the Model Context Protocol (MCP) server for the Persistent-Code tool.
Exposes the knowledge graph functionality through well-defined tools.
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Args:
project_name: Name of the project
storage_dir: Directory to store persistent data
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Args:
transport: Transport protocol ('stdio' or 'http')
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a� Add a new code component to the graph.
Args:
code_text: The actual code
component_type: Type of component (file, class, function, method, variable)
name: Name of the component
status: Implementation status (planned, partial, implemented)
description: Semantic description of the component
Returns:
result: Result including component_id
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aY Update an existing component.
Args:
component_id: ID of the component to update
code_text: New code text (if changed)
status: New implementation status (if changed)
description: New description (if changed)
Returns:
result: Success status
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Args:
source_id: ID of the source component
target_id: ID of the target component
relationship_type: Type of relationship (imports, calls, inherits, contains, depends_on)
metadata: Additional metadata for the relationship
Returns:
result: Result including relationship_id
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Args:
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Returns:
result: Component details
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Args:
component_id: ID of the component
relationship_types: Types of relationships to consider
depth: How many levels of relationships to traverse
Returns:
result: List of related components
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Args:
query: Search query
component_types: Types of components to search
limit: Maximum number of results
Returns:
result: Ranked list of matching components
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Args:
component_id: ID of the component
new_status: New implementation status (planned, partial, implemented)
notes: Optional notes about the status change
Returns:
result: Update confirmation
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dt|�d�W Y d}~S d}~ww )a Retrieve implementation status across the project.
Args:
filters: Filters to apply to components
grouping: How to group the results (e.g., by type)
Returns:
result: Status summary
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dt|�d�W Y d}~S d}~ww )a Suggest logical next components to implement.
Args:
priority_type: How to prioritize tasks (dependencies, complexity)
limit: Maximum number of tasks to suggest
Returns:
result: List of suggested tasks
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aJ Assemble minimal context for a specific task.
Args:
task_description: Description of the task
relevant_components: List of component IDs known to be relevant
max_tokens: Maximum tokens for the context
Returns:
result: Assembled context
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dt|�d�W Y d}~S d}~ww )a Provide context to continue implementing a component.
Args:
component_id: ID of the component to continue implementing
max_tokens: Maximum tokens for the context
Returns:
result: Implementation context
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Args:
component_ids: List of component IDs to include
dependencies_depth: How deep to analyze dependencies
Returns:
result: Ordered implementation plan
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dt|�d�W Y d}~S d}~ww ) a7 Analyze code and update the knowledge graph.
Args:
code_text: The code to analyze
file_path: Path to the file (optional)
component_id: ID of an existing component to update (optional)
Returns:
result: Analysis result
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