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…pers Add ~41 integration tests covering live MCP connections that were previously untested (only unit tests with mocked responses existed). Tests validate: - DatabricksMCPClient list_tools/call_tool against real UC function endpoint - DatabricksOAuthClientProvider with PAT and OAuth-M2M auth paths - get_databricks_resources() and tool name normalization with live tool names - McpServerToolkit (OpenAI SDK) init, get_tools, execute, name prefixing - McpServer (OpenAI Agents SDK) context manager, list_tools, call_tool - DatabricksMCPServer/DatabricksMultiServerMCPClient (LangChain) end-to-end - handle_tool_error and timeout kwargs pass-through in LangChain wrapper All tests gated behind RUN_MCP_INTEGRATION_TESTS=1 env var. Session-scoped caching minimizes live API calls. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…xt manager - test_mcp_resources.py: Use schema_mcp_client (schema-level URL) for get_databricks_resources() tests — single-function URL doesn't match the MCP_URL_PATTERNS regex (expects /catalog/schema, not /catalog/schema/fn) - test_mcp_resources.py: Remove unused DatabricksMCPClient import (ruff F401) - test_langchain_mcp.py: Remove `async with` context manager usage — langchain-mcp-adapters >= 0.1.0 dropped context manager support; use `client = DatabricksMultiServerMCPClient(...)` then `await client.get_tools()` - Apply ruff formatting across all test files Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- test_mcp_resources.py: DatabricksFunction uses .name not .function_name - test_langchain_mcp.py: ainvoke returns list of content dicts, not str; stringify result before checking for 'hello' - test_langchain_mcp.py: handle_tool_error tests verify the attribute is set on tools rather than triggering MCP protocol errors (McpError from missing params bypasses LangChain's handle_tool_error mechanism) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ty renamed `possibly-missing-attribute` to `unresolved-attribute` and checkpoint.py needed `invalid-argument-type` suppression for **dict() kwargs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Core: VS list_tools/call_tool tests, Genie list_tools/call_tool tests - Resources: VS index resources, Genie space resource extraction tests - LangChain: schema-level listing, from_vector_search() URL + tools - OpenAI: schema-level listing, from_vector_search() toolkit + agents - Fixtures: VS/Genie clients cached per-session to minimize API calls - GENIE_SPACE_ID read from env var (matches PR #325 pattern) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…on tests Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…auth, tracing, timeout) 15 new tests covering: VS tool execution at wrapper level (LangChain, OpenAI Toolkit, Agents SDK), VS schema-level listing without index_name, DatabricksMcpHttpClientFactory token refresh, Agents SDK auth paths (PAT + OAuth M2M), MLflow tracing assertion on McpServer.call_tool(), and Agents SDK timeout kwarg validation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…low trace API - LangChain: args_schema can be a plain dict (not Pydantic model), handle both - OpenAI: use mlflow.get_last_active_trace_id() + mlflow.get_trace() instead of mlflow.get_last_active_trace() which doesn't exist in CI's mlflow version Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Resolve ty:ignore comment style conflict in chat_models.py — accept main's version with astral-sh/ty#1479 reference comments. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…ove unused invalid-argument-type ty 0.0.13 renamed the diagnostic code again. Update all ty:ignore comments in chat_models.py and remove now-unnecessary ty:ignore[invalid-argument-type] in checkpoint.py. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- chat_models.py: bare `# ty:ignore` instead of version-specific codes - lakebase.py: add `# ty:ignore[invalid-assignment]` for pool type mismatches - test_openai_mcp.py: add `# ty:ignore[no-matching-overload]` for iter() call Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Core: merge VS + Genie tests into test_mcp_core.py (4 files → 2) - Core: merge redundant list_tools + call_tool assertions (25 → 18 tests) - Resources: merge type + name checks, remove normalization test (9 → 6) - OpenAI: consolidate 13 classes → 4, merge subset tests (27 → 19) - LangChain: consolidate 9 classes → 3, merge subset tests (23 → 16) - Fix VS conftest to dynamically extract param name (consistency with Genie) - Add VS schema-level listing test to core - Total: 76 → 53 tests, all gaps from analysis doc addressed Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Pull request overview
Adds a new MCP integration-test suite to validate the Databricks MCP bridge end-to-end (core client + OpenAI and LangChain wrappers) against a live Databricks MCP server, and includes small ty ignore adjustments to keep CI passing.
Changes:
- Add MCP integration tests for
databricks_mcpcore client (tools listing/execution, resources extraction, auth paths; UC Functions, Vector Search, Genie). - Add MCP integration tests for OpenAI wrappers (
McpServerToolkit, Agents SDKMcpServer) and LangChain wrappers (DatabricksMCPServer,DatabricksMultiServerMCPClient). - Update
tyignore annotations inlakebase.pyand LangChainchat_models.pyto address diagnostic-code churn.
Reviewed changes
Copilot reviewed 7 out of 7 changed files in this pull request and generated 7 comments.
Show a summary per file
| File | Description |
|---|---|
| src/databricks_ai_bridge/lakebase.py | Adds targeted # ty:ignore[invalid-assignment] for pool assignment typing. |
| integrations/langchain/src/databricks_langchain/chat_models.py | Replaces diagnostic-coded ty ignores with bare # ty:ignore in response/chunk conversion paths. |
| databricks_mcp/tests/integration_tests/conftest.py | Adds session-scoped fixtures for live MCP integration testing across UC/VS/Genie. |
| databricks_mcp/tests/integration_tests/test_mcp_core.py | Adds core integration tests for DatabricksMCPClient list/call + auth. |
| databricks_mcp/tests/integration_tests/test_mcp_resources.py | Adds integration tests for get_databricks_resources() normalization and resource typing. |
| integrations/openai/tests/integration_tests/test_openai_mcp.py | Adds OpenAI wrapper integration tests (Toolkit + Agents SDK server) across UC/VS + auth/tracing/timeout. |
| integrations/langchain/tests/integration_tests/test_langchain_mcp.py | Adds LangChain wrapper integration tests across UC/VS + token refresh + kwargs pass-through. |
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integrations/langchain/tests/integration_tests/test_langchain_mcp.py
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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
| """Verify DatabricksMcpHttpClientFactory re-creates auth providers for token refresh.""" | ||
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| def test_sequential_invocations_succeed(self, workspace_client): | ||
| """Two sequential get_tools() calls succeed — proves token refresh works.""" |
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Would the two sequential get_tools() calls not use the same token? Curious how token refresh is triggered here
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I believe the second call gets a new token since the factory creates a new OAuth provider. But youre right that the test doesnt really prove this currently. Will think more about how we can improve the auth token tests. The second test here checks that behavior for new provider, that should be sufficient?
annzhang-db
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Left some comments, in general can we also test more non-happy paths? e.x. I try to use some tool that doesn't exist, or call the tool with the wrong parameters, and make sure that we forward that error back to the user in a meaningful way.
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| with pytest.raises(ExceptionGroup) as exc_info: | ||
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| # Unwrap nested ExceptionGroups to find McpError |
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Note: The pytest match isnt working here since this Error is nested it seems, so unwrapping it a bit.
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| # ============================================================================= | ||
| # DatabricksMcpHttpClientFactory Auth Test |
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This test doesn't replicate what a user would do (they would never directly import and use DatabricksMcpHttpClientFactory), but this test still verifies that each get_tools() call under the hood will create a new OAuth provider (factory behavior).
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annzhang-db
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lgtm - left a few comments, please address them before merge!
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| Uses the first tool from cached_tools_list. | ||
| """ | ||
| tool_name = cached_tools_list[0].name |
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minor: should we check the length of cached_tools_list to make sure that list_tools returned a non-empty list? if the behavior is that list_tools always throws an exception instead of returning an empty list for invalid functions, you can ignore this :) would also be curious what cached_tools_list is if it throws an exception that is not caught, and we are returning tools (might be None)?
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Good catch! I added an assert to make sure both the None case and the empty tools case gets caught and we get a clear assertion failure.
| """Schema-level URL should list at least the echo_message function.""" | ||
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| tools = schema_mcp_client.list_tools() | ||
| except Exception as e: |
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curious why we use Exception here but ExceptionGroup for listing specific UC functions, let's try to catch as specific of exceptions as possible when we skip so that the test fails if there are some new failure modes for list_tools
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Replaced all the Exception catches with ExceptionGroup + skip if the particular error is not found.
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| # -- Listing -- | ||
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| def test_server_lists_tools(self, workspace_client): |
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can we use @pytest.mark.asyncio and async def test_server_lists_tools instead of asyncio.run(_test())? that way pytest-asyncio can handle the event loop for us. i believe we already have this pattern in other tests, so you can refer to those also! would be curious if there was a particular reason why we chose this pattern instead
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Refactored to use @pytest.mark.asyncio instead! Thanks for this catch, the tests look a lot cleaner with this.
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…re catches Review feedback from Ann (comments #3-7): - Remove unused pytestmark from conftest.py (#3) - Add multi-server test: UC + VS in DatabricksMultiServerMCPClient (#4) - Narrow conftest fixture catches: ExceptionGroup with McpError NOT_FOUND check instead of bare Exception — re-raises non-NOT_FOUND errors (#6) - Revert ty/type-ignore changes to non-test files (chat_models.py, lakebase.py, checkpoint.py) — not in scope for this PR (#1, #2) Error path tests across all 3 layers (#7): - Core: bad function (McpError BAD_REQUEST not found), bad tool name (McpError BAD_REQUEST malformed), wrong args (McpError missing parameter) - OpenAI Toolkit: bad function (ValueError wrapping), bad tool (ExceptionGroup), wrong args (ExceptionGroup) - OpenAI Agents: bad function (McpError), bad tool (McpError), wrong args (UserError) - LangChain: bad function (ExceptionGroup > McpError), wrong args (McpError) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Addressed comments, re-ran CI: https://github.com/databricks-eng/ai-oss-integration-tests-runner/actions/runs/22639223391 |
Summary
Adds MCP integration tests covering the core
DatabricksMCPClient, OpenAI wrappers (McpServerToolkit+McpServer), and LangChain wrappers (DatabricksMCPServer+DatabricksMultiServerMCPClient). Tests validate against a live Databricks MCP server across UC Functions, Vector Search, and Genie.64 integration tests (21 core + 25 OpenAI + 18 LangChain), gated behind
RUN_MCP_INTEGRATION_TESTS=1.What's tested
get_databricks_resources()normalization and type correctnesshandle_tool_error,timeoutkwargs pass-through (LangChain)Test plan