use internal errors and not fastmcp errors

Author: bcallender

src/fenic/api/__init__.py

@@ -135,7 +135,7 @@
     "create_mcp_server",
     "run_mcp_server_sync",
     "run_mcp_server_async",
-    "run_mcp_server_asgi"
+    "run_mcp_server_asgi",
     "ToolGenerationConfig",
     "auto_generate_core_tools_from_tables"
 ]

src/fenic/api/mcp/__init__.py

@@ -13,7 +13,7 @@
     "create_mcp_server",
     "run_mcp_server_sync",
     "run_mcp_server_async",
-    "run_mcp_server_asgi"
+    "run_mcp_server_asgi",
     "ToolGenerationConfig",
     "auto_generate_core_tools_from_tables"
 ]

src/fenic/api/mcp/tool_generation.py

@@ -18,7 +18,6 @@
 from typing import Dict, List, Optional, Union
 
 import polars as pl
-from mcp.server.fastmcp.exceptions import ValidationError
 from typing_extensions import Annotated
 
 from fenic.api.dataframe.dataframe import DataFrame
@@ -34,7 +33,7 @@
 from fenic.core._logical_plan.plans import InMemorySource
 from fenic.core._logical_plan.plans.base import LogicalPlan
 from fenic.core._utils.schema import convert_custom_dtype_to_polars
-from fenic.core.error import ConfigurationError
+from fenic.core.error import ConfigurationError, ValidationError
 from fenic.core.mcp.types import DynamicToolDefinition
 from fenic.core.types.datatypes import (
     BooleanType,
@@ -200,7 +199,7 @@ def auto_generate_search_content_tool(
 ) -> DynamicToolDefinition:
     """Create a content search tool for a single dataset (string columns)."""
     if len(datasets) == 0:
-        raise ValueError("Cannot create search content tool: no datasets provided.")
+        raise ValidationError("Cannot create search content tool: no datasets provided.")
 
     name_to_df: Dict[str, DataFrame] = {d.table_name: d.df for d in datasets}
 
@@ -558,7 +557,7 @@ def _ensure_profile_view_for_dataset(spec: DatasetSpec, tool_key: str, refresh:
         catalog = session._session_state.catalog
         if refresh or not catalog.does_view_exist(view_name):
             _materialize_dataset_description(spec.df, spec.table_name, view_name)
-        return catalog.describe_view(view_name)
+        return catalog.get_view_plan(view_name)
 
     def profile_func(
         df_name: Annotated[str | None, "Optional DataFrame name to return a single profile for. To return profiles for all datasets, omit this parameter."] = None,

src/fenic/core/mcp/_server.py

@@ -83,11 +83,20 @@ def __init__(
         annotations = ToolAnnotations(readOnlyHint=True, openWorldHint=False)
         for tool in self.paramaterized_tools:
             tool_fn = self._build_parameterized_tool(tool)
-            self.mcp.tool(annotations=annotations)(tool_fn)
+            self.mcp.tool(
+                annotations=annotations,
+                name=_to_snake_case(tool.name),
+                title=tool.name
+            )(tool_fn)
 
         for tool in self.dynamic_tools:
             tool_fn = self._register_dynamic_callable(tool)
-            self.mcp.tool(name=self._to_snake_case(tool.name), description=tool.description, annotations=annotations)(tool_fn)
+            self.mcp.tool(
+                annotations=annotations,
+                name=_to_snake_case(tool.name),
+                title=tool.name,
+                description=tool.description
+            )(tool_fn)
 
     async def run_async(self, transport: MCPTransport = "http", **kwargs):
         """Run the MCP server asynchronously.
@@ -132,7 +141,8 @@ async def tool_fn(params: Union[ParamsModel, str]) -> MCPResultSet:  # type: ign
                 bound_plan = bind_parameters(tool._parameterized_view, payload, tool.params)
                 async with self._collect_semaphore:
                     pl_df, metrics = await asyncio.to_thread(lambda: self.session_state.execution.collect(bound_plan, n=effective_limit))
-                    logger.info(f"Completed query for {tool.name} in {metrics.execution_time_ms:.0f}ms with {metrics.num_output_rows} result rows.")
+                    logger.info(f"Completed query for {tool.name}")
+                    logger.info(metrics.get_summary())
                     logger.debug(f"Query Details: {params.model_dump_json()}")
 
                 rows_list = pl_df.to_dicts()
@@ -150,7 +160,6 @@ async def tool_fn(params: Union[ParamsModel, str]) -> MCPResultSet:  # type: ign
                 from fastmcp.exceptions import ToolError
                 raise ToolError(f"Fenic server failed to execute tool {tool.name}. Underlying error: {e}") from e
 
-        tool_fn.__name__ = _to_snake_case(tool.name)
         pydantic_schema_description = convert_pydantic_model_to_key_descriptions(ParamsModel)
         tool_fn.__doc__ = "\n\n".join([tool.description, pydantic_schema_description])
         return tool_fn
@@ -177,8 +186,9 @@ async def wrapper(*args, **kwargs) -> MCPResultSet:
                     pl_df, metrics = await asyncio.to_thread(
                         lambda: self.session_state.execution.collect(bound_plan, n=n_rows)
                     )
-                    logger.info(f"Completed query for {tool.name} in {metrics.execution_time_ms:.0f}ms with {metrics.num_output_rows} result rows.")
-                    logger.debug(f"Query Details: {args} {kwargs}")
+                    logger.info(f"Completed query for {tool.name}")
+                    logger.info(metrics.get_summary())
+                    logger.debug(f"Query Details: {args if args else kwargs}")
                 rows_list = pl_df.to_dicts()
                 schema_fields = [{"name": name, "type": str(dtype)} for name, dtype in pl_df.schema.items()]
                 table_format = "structured"

tests/api/mcp/test_tool_generation.py

@@ -9,7 +9,8 @@ def _create_table_with_rows(session, name: str, values: list[int], description:
     df = session.create_dataframe({"id": values})
     # Persist table and optional description through writer (threads description into TableSink)
     if description is not None:
-        df.write.save_as_table(name, mode="overwrite", description=description)
+        df.write.save_as_table(name, mode="overwrite")
+        session.catalog.set_table_description(name, description)
     else:
         # No description: create an empty table with schema only
         session.catalog.create_table(name, Schema([ColumnField("id", IntegerType)]))