Remove screen sdk + sdk + api + docs (#4865)

- remove screen from api, sdk, mcp + docs
Sourced from commit 20d57ae93603fd9741bd4af35d2efadb0fba3227

Author: straeter

everyrow-mcp/README.md

@@ -4,7 +4,7 @@
 
 MCP (Model Context Protocol) server for [everyrow](https://everyrow.io): agent ops at spreadsheet scale.
 
-This server exposes everyrow's core operations as MCP tools, allowing LLM applications to classify, screen, rank, dedupe, merge, forecast, and run agents on CSV files.
+This server exposes everyrow's core operations as MCP tools, allowing LLM applications to classify, rank, dedupe, merge, forecast, and run agents on CSV files.
 
 **All tools operate on local CSV files.** Provide absolute file paths as input, and transformed results are written to new CSV files at your specified output path.
 
@@ -60,19 +60,6 @@ All operations follow an async pattern:
 
 ## Available Tools
 
-### everyrow_screen
-
-Filter CSV rows based on criteria that require judgment.
-
-```
-Parameters:
-- task: Natural language description of screening criteria
-- input_csv: Absolute path to input CSV
-- response_schema: (optional) JSON schema for custom response fields
-```
-
-Example: Filter job postings for "remote-friendly AND senior-level AND salary disclosed"
-
 ### everyrow_rank
 
 Score and sort CSV rows based on qualitative criteria.

everyrow-mcp/manifest.json

@@ -29,10 +29,6 @@
     }
   },
   "tools": [
-    {
-      "name": "everyrow_screen",
-      "description": "Filter rows in a CSV file based on any criteria."
-    },
     {
       "name": "everyrow_rank",
       "description": "Score and sort rows in a CSV file based on any criteria."
@@ -113,7 +109,7 @@
       "python": ">=3.12"
     }
   },
-  "keywords": ["everyrow", "dataframe", "csv", "ai", "data-processing", "classify", "dedupe", "merge", "rank", "screen", "forecast"],
+  "keywords": ["everyrow", "dataframe", "csv", "ai", "data-processing", "classify", "dedupe", "merge", "rank", "forecast"],
   "license": "MIT",
   "privacy_policies": ["https://futuresearch.ai/privacy/"]
 }

everyrow-mcp/src/everyrow_mcp/app.py

@@ -82,7 +82,7 @@ async def no_auth_http_lifespan(_server: FastMCP):
 ## Workflow
 1. **Ingest data** — pass `data` (inline list of dicts) or an `artifact_id` \
 (from `everyrow_upload_data` or `everyrow_request_upload_url`) to any processing tool.
-2. **Submit** — call a processing tool (everyrow_agent, everyrow_screen, \
+2. **Submit** — call a processing tool (everyrow_agent, everyrow_classify, \
 everyrow_rank, everyrow_dedupe, everyrow_merge, everyrow_forecast). \
 It returns a task_id immediately.
 3. **Poll** — call `everyrow_progress(task_id)` repeatedly until the task completes. \

everyrow-mcp/src/everyrow_mcp/models.py

@@ -78,25 +78,6 @@ def _validate_response_schema(schema: dict[str, Any] | None) -> dict[str, Any] |
     return schema
 
 
-def _validate_screen_response_schema(
-    schema: dict[str, Any] | None,
-) -> dict[str, Any] | None:
-    """Validate screen response_schema includes at least one boolean property."""
-    validated_schema = _validate_response_schema(schema)
-    if validated_schema is None:
-        return None
-
-    properties = validated_schema["properties"]
-    has_boolean_property = any(
-        isinstance(field_def, dict) and field_def.get("type") == "boolean"
-        for field_def in properties.values()
-    )
-    if not has_boolean_property:
-        raise ValueError("response_schema must include at least one boolean property")
-
-    return validated_schema
-
-
 def _schema_to_model(name: str, schema: dict[str, Any]) -> type[BaseModel]:
     """Convert a JSON schema dict to a dynamic Pydantic model.
 
@@ -314,28 +295,6 @@ def validate_response_schema(
         return _validate_response_schema(v)
 
 
-class ScreenInput(_SingleSourceInput):
-    """Input for the screen operation."""
-
-    model_config = ConfigDict(str_strip_whitespace=True, extra="forbid")
-
-    task: str = Field(
-        ..., description="Natural language screening criteria.", min_length=1
-    )
-    response_schema: dict[str, Any] | None = Field(
-        default=None,
-        description="Optional JSON schema for the response model. "
-        "Must include at least one boolean property — screen uses the boolean field to filter rows into pass/fail.",
-    )
-
-    @field_validator("response_schema")
-    @classmethod
-    def validate_response_schema(
-        cls, v: dict[str, Any] | None
-    ) -> dict[str, Any] | None:
-        return _validate_screen_response_schema(v)
-
-
 class DedupeInput(_SingleSourceInput):
     """Input for the dedupe operation."""
 

everyrow-mcp/src/everyrow_mcp/tool_helpers.py

@@ -321,7 +321,7 @@ async def create_tool_response(
     return [main_content]
 
 
-_UI_EXCLUDE: set[str] = {"is_terminal", "is_screen", "task_type", "error", "started_at"}
+_UI_EXCLUDE: set[str] = {"is_terminal", "task_type", "error", "started_at"}
 
 
 class TaskState(BaseModel):
@@ -349,11 +349,6 @@ def is_terminal(self) -> bool:
             TaskStatus.REVOKED,
         )
 
-    @computed_field
-    @property
-    def is_screen(self) -> bool:
-        return self._response.task_type == PublicTaskType.SCREEN
-
     @computed_field
     @property
     def task_type(self) -> PublicTaskType:
@@ -441,10 +436,7 @@ def progress_message(  # noqa: PLR0912
             if self.error:
                 return f"Task {self.status.value}: {self.error}"
             if self.status == TaskStatus.COMPLETED:
-                if self.is_screen:
-                    completed_msg = f"Screening complete ({self.elapsed_s}s)."
-                else:
-                    completed_msg = f"Completed: {self.completed}/{self.total} ({self.failed} failed) in {self.elapsed_s}s."
+                completed_msg = f"Completed: {self.completed}/{self.total} ({self.failed} failed) in {self.elapsed_s}s."
                 if settings.is_http:
                     if self.total <= settings.auto_page_size_threshold:
                         next_call = dedent(f"""\
@@ -467,11 +459,6 @@ def progress_message(  # noqa: PLR0912
                 return f"{completed_msg}\n{next_call}"
             return f"Task {self.status.value}. Report the error to the user."
 
-        if self.is_screen:
-            return dedent(f"""\
-                Screen running ({self.elapsed_s}s elapsed).
-                Immediately call everyrow_progress(task_id='{task_id}').""")
-
         fail_part = f", {self.failed} failed" if self.failed else ""
         cursor_arg = f", cursor='{cursor}'" if cursor else ""
         msg = dedent(f"""\

everyrow-mcp/src/everyrow_mcp/tools.py

@@ -23,7 +23,6 @@
     forecast_async,
     merge_async,
     rank_async,
-    screen_async,
     single_agent_async,
 )
 from everyrow.session import create_session, get_session_url, list_sessions
@@ -48,7 +47,6 @@
     MergeInput,
     ProgressInput,
     RankInput,
-    ScreenInput,
     SingleAgentInput,
     StdioResultsInput,
     UploadDataInput,
@@ -509,88 +507,6 @@ async def everyrow_rank(params: RankInput, ctx: EveryRowContext) -> list[TextCon
     )
 
 
-@mcp.tool(
-    name="everyrow_screen",
-    structured_output=False,
-    annotations=ToolAnnotations(
-        title="Filter Rows by Criteria",
-        readOnlyHint=False,
-        destructiveHint=False,
-        idempotentHint=False,
-        openWorldHint=True,
-    ),
-)
-async def everyrow_screen(
-    params: ScreenInput, ctx: EveryRowContext
-) -> list[TextContent]:
-    """Filter rows in a CSV file based on any criteria.
-
-    Dispatches web agents to research the criteria to filter the entities in the
-    table. Conducts research, and can also apply judgment to the results if the
-    criteria are qualitative.
-
-    Screen produces a boolean pass/fail verdict per row. If you provide a custom
-    response_schema, it MUST include at least one boolean property (e.g.
-    ``{"passes": {"type": "boolean"}}``). If the screening criteria need more than
-    a yes/no answer (e.g. a three-way classification), use everyrow_agent instead.
-
-    Examples:
-    - "Is this job posting remote-friendly AND senior-level AND salary disclosed?"
-    - "Is this vendor financially stable AND does it have good security practices?"
-    - "Is this lead likely to need our product based on company description?"
-
-    This function submits the task and returns immediately with a task_id and session_url.
-
-    Then immediately call everyrow_progress(task_id) to monitor.
-    Once the task is completed, call everyrow_results to save the output.
-
-    Args:
-        params: ScreenInput
-
-    Returns:
-        Success message containing task_id for monitoring progress
-    """
-    logger.info(
-        "everyrow_screen: task=%.80s rows=%s",
-        params.task,
-        len(params.data) if params.data else "artifact",
-    )
-    log_client_info(ctx, "everyrow_screen")
-    client = _get_client(ctx)
-
-    input_data = params._aid_or_dataframe
-
-    response_model: type[BaseModel] | None = None
-    if params.response_schema:
-        response_model = _schema_to_model("ScreenResult", params.response_schema)
-
-    async with create_session(
-        client=client, session_id=params.session_id, name=params.session_name
-    ) as session:
-        session_url = session.get_url()
-        session_id_str = str(session.session_id)
-        cohort_task = await screen_async(
-            task=params.task,
-            session=session,
-            input=input_data,
-            response_model=response_model,
-        )
-        task_id = str(cohort_task.task_id)
-        total = len(input_data) if isinstance(input_data, pd.DataFrame) else 0
-
-    return await create_tool_response(
-        task_id=task_id,
-        session_url=session_url,
-        label=f"Submitted: {total} rows for screening."
-        if total
-        else "Submitted: artifact for screening.",
-        token=client.token,
-        total=total,
-        mcp_server_url=ctx.request_context.lifespan_context.mcp_server_url,
-        session_id=session_id_str,
-    )
-
-
 @mcp.tool(
     name="everyrow_dedupe",
     structured_output=False,
@@ -922,7 +838,7 @@ async def everyrow_upload_data(
 ) -> list[TextContent]:
     """Upload data from a URL or local file. Returns an artifact_id for use in processing tools.
 
-    Use this tool to ingest data before calling everyrow_agent, everyrow_screen,
+    Use this tool to ingest data before calling everyrow_agent,
     everyrow_rank, everyrow_dedupe, everyrow_merge, everyrow_classify, or everyrow_forecast.
 
     Supported sources:
@@ -1090,7 +1006,7 @@ async def everyrow_progress(
     summaries: list[dict[str, Any]] | None = None
     cursor: str | None = params.cursor
 
-    if not ts.is_terminal and not ts.is_screen:
+    if not ts.is_terminal:
         if ts.completed > 0:
             (
                 (partial_rows, rows_cursor),

everyrow-mcp/src/everyrow_mcp/utils.py

@@ -433,7 +433,7 @@ def resolve_output_path(output_path: str, input_path: str, prefix: str) -> Path:
     Args:
         output_path: The output path (directory or full file path)
         input_path: The input file path (used to generate output filename)
-        prefix: Prefix to add to the generated filename (e.g., 'screened', 'ranked')
+        prefix: Prefix to add to the generated filename (e.g., 'classified', 'ranked')
 
     Returns:
         Full path to the output file