remove leann_index from MCP interface
Users should use CLI command 'leann build' to create indexes first. MCP now only provides search functionality: - leann_search: search existing indexes - leann_status: check index health - leann_list: list available indexes This separates index creation (CLI) from search (Claude Code). 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Andy Lee
@@ -23,57 +23,6 @@ def handle_request(request):
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"id": request.get("id"),
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"id": request.get("id"),
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"result": {
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"result": {
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"tools": [
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"tools": [
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{
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"name": "leann_index",
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"description": """🏗️ Index a codebase for intelligent code search and understanding.
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🎯 **When to use**: Before analyzing, modifying, or understanding any codebase
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📁 **What it does**: Creates a semantic search index of code files and documentation
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⚡ **Why it's useful**: Enables fast, intelligent searches like "authentication logic", "error handling patterns", "API endpoints"
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This is your first step for any serious codebase work - think of it as giving yourself superpowers to understand and navigate code.""",
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"inputSchema": {
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"type": "object",
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"properties": {
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"index_name": {
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"type": "string",
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"description": "Name for the new index. Use descriptive names like 'my-project' or 'backend-api'.",
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},
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"docs_path": {
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"type": "string",
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"description": "Path to the directory containing code/documents to index. Can be relative (e.g., './src') or absolute.",
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},
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"force": {
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"type": "boolean",
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"default": False,
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"description": "Force rebuild of existing index. Use when you want to completely reindex and overwrite existing data.",
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},
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"backend": {
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"type": "string",
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"enum": ["hnsw", "diskann"],
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"default": "hnsw",
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"description": "Vector index backend: 'hnsw' for balanced performance, 'diskann' for large-scale datasets.",
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},
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"embedding_model": {
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"type": "string",
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"default": "facebook/contriever",
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"description": "Embedding model to use. Popular options: 'facebook/contriever', 'sentence-transformers/all-MiniLM-L6-v2'",
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},
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"file_types": {
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"type": "array",
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"items": {"type": "string"},
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"description": "File extensions to include (e.g., ['.py', '.js', '.ts', '.md']). If not specified, uses default supported types.",
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},
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"ignore_patterns": {
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"type": "array",
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"items": {"type": "string"},
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"default": [],
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"description": "Patterns to ignore during indexing (e.g., ['node_modules', '__pycache__', '*.tmp', 'dist']). Common patterns are automatically ignored.",
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},
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},
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"required": ["index_name", "docs_path"],
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},
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},
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{
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{
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"name": "leann_search",
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"name": "leann_search",
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"description": """🔍 Search code using natural language - like having a coding assistant who knows your entire codebase!
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"description": """🔍 Search code using natural language - like having a coding assistant who knows your entire codebase!
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@@ -142,70 +91,7 @@ This is your first step for any serious codebase work - think of it as giving yo
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args = request["params"].get("arguments", {})
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args = request["params"].get("arguments", {})
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try:
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try:
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if tool_name == "leann_index":
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if tool_name == "leann_search":
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# Validate required parameters
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if not args.get("index_name") or not args.get("docs_path"):
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": "Error: Both index_name and docs_path are required",
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}
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]
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},
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}
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# Validate docs_path exists
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import os
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docs_path = args["docs_path"]
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if not os.path.exists(docs_path):
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"Error: Path '{docs_path}' does not exist",
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}
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]
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},
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}
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# Build index command
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cmd = [
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"leann",
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"build",
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args["index_name"],
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"--docs",
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docs_path,
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"--backend",
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args.get("backend", "hnsw"),
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"--embedding-model",
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args.get("embedding_model", "facebook/contriever"),
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]
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# Add force flag if specified
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if args.get("force", False):
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cmd.append("--force")
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# Add file types if specified (now as array)
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file_types = args.get("file_types")
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if file_types and isinstance(file_types, list):
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cmd.extend(["--file-types", ",".join(file_types)])
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# Add ignore patterns if specified
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ignore_patterns = args.get("ignore_patterns", [])
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if ignore_patterns and isinstance(ignore_patterns, list):
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# For now, pass as comma-separated string - CLI can be enhanced later
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cmd.extend(["--ignore", ",".join(ignore_patterns)])
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result = subprocess.run(cmd, capture_output=True, text=True)
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elif tool_name == "leann_search":
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# Validate required parameters
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# Validate required parameters
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if not args.get("index_name") or not args.get("query"):
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if not args.get("index_name") or not args.get("query"):
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return {
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return {
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