Skip to main content
Glama
santoshray02

CSV Editor

by santoshray02

get_auto_save_status

Check if auto-save is active for a CSV editing session to prevent data loss during large file processing.

Instructions

Get auto-save status for a session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Main handler function that retrieves the auto-save status for a given session by fetching the session and calling its get_auto_save_status method, then wrapping the result in OperationResult.
    async def get_auto_save_status(
        session_id: str,
        ctx: Context = None
    ) -> Dict[str, Any]:
        """
        Get auto-save status for a session.
        
        Args:
            session_id: Session identifier
            ctx: FastMCP context
            
        Returns:
            Dict with auto-save status
        """
        try:
            manager = get_session_manager()
            session = manager.get_session(session_id)
            
            if not session:
                return OperationResult(
                    success=False,
                    message="Session not found",
                    error=f"No session with ID: {session_id}"
                ).model_dump()
            
            status = session.get_auto_save_status()
            
            if ctx:
                await ctx.info(f"Auto-save status retrieved for session {session_id}")
            
            return OperationResult(
                success=True,
                message="Auto-save status retrieved",
                session_id=session_id,
                data=status
            ).model_dump()
            
        except Exception as e:
            logger.error(f"Error getting auto-save status: {str(e)}")
            if ctx:
                await ctx.error(f"Failed to get auto-save status: {str(e)}")
            return OperationResult(
                success=False,
                message="Failed to get auto-save status",
                error=str(e)
            ).model_dump()
  • MCP tool registration using @mcp.tool decorator. This is the entry point for the tool, which delegates to the implementation in auto_save_operations.py.
    @mcp.tool
    async def get_auto_save_status(
        session_id: str,
        ctx: Context = None
    ) -> Dict[str, Any]:
        """Get auto-save status for a session."""
        return await _get_auto_save_status(session_id, ctx)
  • Helper method on CSVSession model that returns the auto-save status from the AutoSaveManager instance.
    def get_auto_save_status(self) -> Dict[str, Any]:
        """Get current auto-save status."""
        return self.auto_save_manager.get_status()
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it's a 'Get' operation, implying read-only behavior, but doesn't clarify whether it requires specific permissions, what the output format is (though an output schema exists), or if there are rate limits. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose ('Get auto-save status'). There is no wasted wording, and it directly communicates the tool's function without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete parameter guidance, it lacks details on behavioral aspects like error conditions or dependencies. It meets a basic threshold but could be more informative.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description mentions 'for a session', which aligns with the 'session_id' parameter in the schema. However, schema description coverage is 0%, so the schema provides no details about the parameter. The description adds minimal context (it's session-related) but doesn't explain what a session_id is, its format, or where to obtain it. This partially compensates but remains vague.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get') and resource ('auto-save status for a session'), making the purpose immediately understandable. It doesn't explicitly differentiate from siblings like 'configure_auto_save' or 'disable_auto_save', but the verb 'Get' implies a read operation rather than configuration or mutation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an active session), exclusions, or related tools like 'get_session_info' that might provide overlapping information. The agent must infer usage from context alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/santoshray02/csv-editor'

If you have feedback or need assistance with the MCP directory API, please join our Discord server