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DeepaRajareddy

Redshift MCP Server

redshift_describe_table

Retrieve column definitions and structure for Amazon Redshift tables to understand data types and schema organization.

Instructions

Get the column definitions for a table.

Args:
    table_name: Name of the table
    schema: Schema name (default: "public")

Returns:
    JSON description of columns

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes
schemaNopublic

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'redshift_describe_table' MCP tool. It is registered via the @mcp.tool() decorator. The function builds a SQL query to retrieve column metadata from the information_schema and delegates execution to the 'redshift_query' tool, returning the JSON results.
    @mcp.tool()
    def redshift_describe_table(table_name: str, schema: str = "public") -> str:
        """
        Get the column definitions for a table.
        
        Args:
            table_name: Name of the table
            schema: Schema name (default: "public")
        
        Returns:
            JSON description of columns
        """
        sql = f"""
        SELECT column_name, data_type, is_nullable, column_default
        FROM information_schema.columns
        WHERE table_schema = '{schema}'
        AND table_name = '{table_name}'
        ORDER BY ordinal_position
        """
        return redshift_query(sql)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves column definitions but lacks details on permissions, error handling, rate limits, or whether it's a read-only operation. This leaves gaps in understanding the tool's behavior beyond basic functionality.

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 well-structured and concise, using a clear purpose statement followed by bullet points for arguments and returns. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers the purpose, parameters, and return format. The output schema existence means the description doesn't need to detail return values, but it could benefit from more behavioral context.

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 description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'table_name' is the name of the table and 'schema' is the schema name with a default of 'public', clarifying parameter purposes that aren't covered in the schema itself.

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 tool's purpose with a specific verb ('Get') and resource ('column definitions for a table'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'redshift_list_tables' or 'redshift_get_sample_data', which might offer related functionality.

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 sibling tools like 'redshift_list_tables' for listing tables or 'redshift_get_sample_data' for retrieving data samples, leaving the agent to infer usage context without explicit direction.

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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