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get_server_info

Discover server capabilities, available tools, supported file formats, and resource limits to plan workflows with DataBeak's CSV data processing tools.

Instructions

Get DataBeak server capabilities and supported operations.

Returns server version, available tools, supported file formats, and resource limits. Use to discover what operations are available before planning workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesServer name and identification
successNoWhether operation completed successfully
versionYesCurrent server version
descriptionYesServer description and purpose
capabilitiesYesAvailable operations organized by category
max_download_size_mbYesMaximum download size from URLs in MB
session_timeout_minutesYesDefault session timeout in minutes

Implementation Reference

  • The primary handler function implementing the get_server_info tool. It retrieves server settings and constructs a detailed ServerInfoResult with capabilities categorized by data I/O, manipulation, analysis, validation, session management, and null handling.
    async def get_server_info(
        ctx: Annotated[Context, Field(description="FastMCP context for progress reporting")],
    ) -> ServerInfoResult:
        """Get DataBeak server capabilities and supported operations.
    
        Returns server version, available tools, supported file formats, and resource limits. Use to
        discover what operations are available before planning workflows.
        """
        await ctx.info("Retrieving DataBeak server information")
    
        # Get current configuration settings
        settings = get_settings()
    
        server_info = ServerInfoResult(
            name="DataBeak",
            version=__version__,
            description="A comprehensive MCP server for CSV file operations and data analysis",
            capabilities={
                "data_io": [
                    "load_csv_from_url",
                    "load_csv_from_content",
                ],
                "data_manipulation": [
                    "filter_rows",
                    "sort_data",
                    "select_columns",
                    "rename_columns",
                    "add_column",
                    "remove_columns",
                    "change_column_type",
                    "fill_missing_values",
                    "remove_duplicates",
                    "null_value_support",  # Explicitly mention null support
                ],
                "data_analysis": [
                    "get_statistics",
                    "correlation_matrix",
                    "group_by_aggregate",
                    "value_counts",
                    "detect_outliers",
                    "profile_data",
                ],
                "data_validation": [
                    "validate_schema",
                    "check_data_quality",
                    "find_anomalies",
                ],
                "session_management": [
                    "multi_session_support",
                    "session_isolation",
                    "auto_cleanup",
                ],
                "null_handling": [
                    "json_null_support",
                    "python_none_support",
                    "pandas_nan_compatibility",
                    "null_value_insertion",
                    "null_value_updates",
                ],
            },
            max_download_size_mb=settings.max_download_size_mb,
            session_timeout_minutes=settings.session_timeout // 60,
        )
    
        await ctx.info("Server information retrieved successfully")
    
        return server_info
  • Pydantic model defining the structured output schema for the get_server_info tool response, including server metadata and capabilities.
    class ServerInfoResult(BaseToolResponse):
        """Response model for server information and capabilities."""
    
        name: str = Field(description="Server name and identification")
        version: str = Field(description="Current server version")
        description: str = Field(description="Server description and purpose")
        capabilities: dict[str, list[str]] = Field(
            description="Available operations organized by category",
        )
        max_download_size_mb: int = Field(description="Maximum download size from URLs in MB")
        session_timeout_minutes: int = Field(description="Default session timeout in minutes")
  • Registration of the get_server_info handler as an MCP tool on the system_server FastMCP instance with explicit name.
    system_server.tool(name="get_server_info")(get_server_info)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns specific information (server version, available tools, etc.) and implies it's a read-only discovery operation. However, it doesn't mention potential behavioral aspects like authentication requirements, rate limits, or whether it's idempotent. The description adds basic context but lacks richer behavioral details.

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 front-loaded with the core purpose in the first sentence, followed by a concise usage guideline. Both sentences earn their place by providing essential information without redundancy. The structure is logical and efficiently communicates the tool's role.

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

Completeness4/5

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

Given the tool's low complexity (0 parameters, no annotations, but with an output schema), the description is mostly complete. It explains the purpose and usage well, and the output schema will handle return values. However, for a tool with no annotations, it could benefit from more behavioral context (e.g., idempotency, side effects), though the output schema mitigates some gaps.

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

Parameters4/5

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

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain parameters, but it implicitly confirms there are no required inputs by not mentioning any. This aligns perfectly with the schema, so a baseline of 4 is appropriate for a parameterless tool.

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

Purpose5/5

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('DataBeak server capabilities and supported operations'), and distinguishes it from all sibling tools which are data manipulation operations. It explicitly lists what information is returned (server version, available tools, etc.), making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: 'Use to discover what operations are available before planning workflows.' This clearly indicates it's for initial discovery and planning, distinguishing it from sibling tools that perform actual data operations. No misleading or missing guidance is present.

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

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