code-context-mcp
Version:
MCP server for semantic code search powered by MongoDB Atlas Vector Search and Voyage AI embeddings
74 lines • 2.11 kB
TypeScript
import { Context } from "@zilliz/claude-context-core";
import { SnapshotManager } from "./snapshot.js";
export declare class ToolHandlers {
private context;
private snapshotManager;
private indexingStats;
private currentWorkspace;
constructor(context: Context, snapshotManager: SnapshotManager);
/**
* Sync indexed codebases from Zilliz Cloud collections
* This method fetches all collections from the vector database,
* gets the first document from each collection to extract codebasePath from metadata,
* and updates the snapshot with discovered codebases.
*
* Logic: Compare mcp-codebase-snapshot.json with zilliz cloud collections
* - If local snapshot has extra directories (not in cloud), remove them
* - If local snapshot is missing directories (exist in cloud), ignore them
*/
private syncIndexedCodebasesFromCloud;
handleIndexCodebase(args: any): Promise<{
content: {
type: string;
text: any;
}[];
isError: boolean;
} | {
content: {
type: string;
text: string;
}[];
isError?: undefined;
}>;
private startBackgroundIndexing;
handleSearchCode(args: any): Promise<{
content: {
type: string;
text: string;
}[];
isError: boolean;
} | {
content: {
type: string;
text: any;
}[];
isError?: undefined;
}>;
handleClearIndex(args: any): Promise<{
content: {
type: string;
text: string;
}[];
isError: boolean;
} | {
content: {
type: string;
text: any;
}[];
isError?: undefined;
}>;
handleGetIndexingStatus(args: any): Promise<{
content: {
type: string;
text: string;
}[];
isError: boolean;
} | {
content: {
type: string;
text: string;
}[];
isError?: undefined;
}>;
}
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