From 1e5d05e36a20b52307a90aa23d21869e42b4bab2 Mon Sep 17 00:00:00 2001
From: Andy Lee <andylizf@outlook.com>
Date: Sat, 9 Aug 2025 19:28:40 -0700
Subject: [PATCH] remove leann_index from MCP interface
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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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>
---
 packages/leann-core/src/leann/mcp.py | 116 +--------------------------
 1 file changed, 1 insertion(+), 115 deletions(-)

diff --git a/packages/leann-core/src/leann/mcp.py b/packages/leann-core/src/leann/mcp.py
index f67c3e3..92f79e3 100755
--- a/packages/leann-core/src/leann/mcp.py
+++ b/packages/leann-core/src/leann/mcp.py
@@ -23,57 +23,6 @@ def handle_request(request):
             "id": request.get("id"),
             "result": {
                 "tools": [
-                    {
-                        "name": "leann_index",
-                        "description": """🏗️ Index a codebase for intelligent code search and understanding.
-
-🎯 **When to use**: Before analyzing, modifying, or understanding any codebase
-📁 **What it does**: Creates a semantic search index of code files and documentation
-⚡ **Why it's useful**: Enables fast, intelligent searches like "authentication logic", "error handling patterns", "API endpoints"
-
-This is your first step for any serious codebase work - think of it as giving yourself superpowers to understand and navigate code.""",
-                        "inputSchema": {
-                            "type": "object",
-                            "properties": {
-                                "index_name": {
-                                    "type": "string",
-                                    "description": "Name for the new index. Use descriptive names like 'my-project' or 'backend-api'.",
-                                },
-                                "docs_path": {
-                                    "type": "string",
-                                    "description": "Path to the directory containing code/documents to index. Can be relative (e.g., './src') or absolute.",
-                                },
-                                "force": {
-                                    "type": "boolean",
-                                    "default": False,
-                                    "description": "Force rebuild of existing index. Use when you want to completely reindex and overwrite existing data.",
-                                },
-                                "backend": {
-                                    "type": "string",
-                                    "enum": ["hnsw", "diskann"],
-                                    "default": "hnsw",
-                                    "description": "Vector index backend: 'hnsw' for balanced performance, 'diskann' for large-scale datasets.",
-                                },
-                                "embedding_model": {
-                                    "type": "string",
-                                    "default": "facebook/contriever",
-                                    "description": "Embedding model to use. Popular options: 'facebook/contriever', 'sentence-transformers/all-MiniLM-L6-v2'",
-                                },
-                                "file_types": {
-                                    "type": "array",
-                                    "items": {"type": "string"},
-                                    "description": "File extensions to include (e.g., ['.py', '.js', '.ts', '.md']). If not specified, uses default supported types.",
-                                },
-                                "ignore_patterns": {
-                                    "type": "array",
-                                    "items": {"type": "string"},
-                                    "default": [],
-                                    "description": "Patterns to ignore during indexing (e.g., ['node_modules', '__pycache__', '*.tmp', 'dist']). Common patterns are automatically ignored.",
-                                },
-                            },
-                            "required": ["index_name", "docs_path"],
-                        },
-                    },
                     {
                         "name": "leann_search",
                         "description": """🔍 Search code using natural language - like having a coding assistant who knows your entire codebase!
@@ -142,70 +91,7 @@ This is your first step for any serious codebase work - think of it as giving yo
         args = request["params"].get("arguments", {})
 
         try:
-            if tool_name == "leann_index":
-                # Validate required parameters
-                if not args.get("index_name") or not args.get("docs_path"):
-                    return {
-                        "jsonrpc": "2.0",
-                        "id": request.get("id"),
-                        "result": {
-                            "content": [
-                                {
-                                    "type": "text",
-                                    "text": "Error: Both index_name and docs_path are required",
-                                }
-                            ]
-                        },
-                    }
-
-                # Validate docs_path exists
-                import os
-
-                docs_path = args["docs_path"]
-                if not os.path.exists(docs_path):
-                    return {
-                        "jsonrpc": "2.0",
-                        "id": request.get("id"),
-                        "result": {
-                            "content": [
-                                {
-                                    "type": "text",
-                                    "text": f"Error: Path '{docs_path}' does not exist",
-                                }
-                            ]
-                        },
-                    }
-
-                # Build index command
-                cmd = [
-                    "leann",
-                    "build",
-                    args["index_name"],
-                    "--docs",
-                    docs_path,
-                    "--backend",
-                    args.get("backend", "hnsw"),
-                    "--embedding-model",
-                    args.get("embedding_model", "facebook/contriever"),
-                ]
-
-                # Add force flag if specified
-                if args.get("force", False):
-                    cmd.append("--force")
-
-                # Add file types if specified (now as array)
-                file_types = args.get("file_types")
-                if file_types and isinstance(file_types, list):
-                    cmd.extend(["--file-types", ",".join(file_types)])
-
-                # Add ignore patterns if specified
-                ignore_patterns = args.get("ignore_patterns", [])
-                if ignore_patterns and isinstance(ignore_patterns, list):
-                    # For now, pass as comma-separated string - CLI can be enhanced later
-                    cmd.extend(["--ignore", ",".join(ignore_patterns)])
-                result = subprocess.run(cmd, capture_output=True, text=True)
-
-            elif tool_name == "leann_search":
+            if tool_name == "leann_search":
                 # Validate required parameters
                 if not args.get("index_name") or not args.get("query"):
                     return {