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remove leann_index from MCP interface

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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>
This commit is contained in:
Andy Lee
2025-08-09 19:28:40 -07:00
parent 5d21f5bd9d
commit 1e5d05e36a
1 changed files with 1 additions and 115 deletions
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116
packages/leann-core/src/leann/mcp.py
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@@ -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 {