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a76cb6feaf
The mcp_tool_search virtual tool only did substring token matching, so a native MCP client asking for "FX" could not find a tool described as "foreign exchange rates" even though the same catalog is ranked by embeddings on /responses and /chat/completions. Adds litellm_settings.mcp_tool_search (embedding_model, top_k, similarity_threshold, core_tools). With an embedding model the caller's authorized catalog from _list_mcp_tools is ranked by cosine similarity of name plus description; configured core tools the caller can reach come first and do not consume top_k. Without an embedding model the keyword fallback keeps the old behavior. Settings are hot-reloadable from the DB, exposed on /get and /update mcp_tool_search_settings, and editable from the Admin UI under MCP Servers > Tool Search. The embedding index is shared with agent_search via a new SemanticTextIndex. Resolves LIT-6751 Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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| .. | ||
| auth | ||
| __init__.py | ||
| test_a2a_endpoints.py | ||
| test_a2a_version_e2e.py | ||
| test_agent_header_isolation.py | ||
| test_agent_headers.py | ||
| test_agent_rbac.py | ||
| test_agent_registry.py | ||
| test_agent_search.py | ||
| test_databricks_oauth.py | ||
| test_endpoints.py | ||
| test_model_list_helpers.py | ||