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blitzstermayank

Teradata MCP Server

qlty_rowsWithMissingValues

Identify and retrieve rows containing missing values in a specified Teradata table column to support data quality analysis and cleaning.

Instructions

Get the rows with missing values in a table.

Arguments: database_name - name of the database table_name - table name to analyze column_name - column name to analyze

Returns: ResponseType: formatted response with query results + metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_nameYes
table_nameYes
column_nameYes

Implementation Reference

  • Handler function implementing the qlty_rowsWithMissingValues tool. It queries the Teradata database using TD_getRowsWithMissingValues to fetch rows with missing values in the specified column and returns formatted JSON response with metadata.
    #------------------ Tool  ------------------#
    # Get Rows with Miissing Values tool
    def handle_qlty_rowsWithMissingValues(
        conn: TeradataConnection,
        database_name: str | None,
        table_name: str,
        column_name: str,
        *args,
        **kwargs
    ):
        """
        Get the rows with missing values in a table.
    
        Arguments:
          database_name - name of the database
          table_name - table name to analyze
          column_name - column name to analyze
    
        Returns:
          ResponseType: formatted response with query results + metadata
        """
        logger.debug(f"Tool: handle_qlty_rowsWithMissingValues: Args: table_name: {database_name}.{table_name}, column_name: {column_name}")
    
        if database_name is not None:
                table_name = f"{database_name}.{table_name}"
        with conn.cursor() as cur:
            rows = cur.execute(f"select * from TD_getRowsWithMissingValues ( ON {table_name} AS InputTable USING TargetColumns ('[{column_name}]')) AS dt;")
            data = rows_to_json(cur.description, rows.fetchall())
            metadata = {
                "tool_name": "qlty_rowsWithMissingValues",
                "database_name": database_name,
                "table_name": table_name,
                "column_name": column_name,
                "rows_with_missing_values": len(data)
            }
            logger.debug(f"Tool: handle_qlty_rowsWithMissingValues: Metadata: {metadata}")
            return create_response(data, metadata)
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 the tool returns 'formatted response with query results + metadata,' which hints at output format but lacks details on permissions, rate limits, or side effects (e.g., whether it's read-only or has performance impacts). For a tool with no annotations, this is insufficient to fully inform usage.

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

Conciseness4/5

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 structured sections for arguments and returns. It's efficient with minimal waste, though the 'Arguments' and 'Returns' sections could be integrated more seamlessly. Overall, it's appropriately sized and clear.

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

Completeness2/5

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

Given the complexity (3 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, parameter usage, and output specifics beyond a vague mention of 'formatted response.' For a data analysis tool with siblings, more context is needed to ensure proper tool selection and invocation.

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

Parameters2/5

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

Schema description coverage is 0%, meaning the input schema provides no descriptions for the three parameters. The description lists the parameters ('database_name', 'table_name', 'column_name') but doesn't add meaningful semantics beyond their names, such as format examples, constraints, or how they interact. This fails to compensate for the low schema coverage, leaving parameters poorly documented.

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: 'Get the rows with missing values in a table.' This specifies the verb ('Get') and resource ('rows with missing values'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'qlty_missingValues' or 'qlty_columnSummary', which might offer similar functionality, so it doesn't reach a perfect score.

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 'qlty_missingValues' (which might summarize missing values) or 'qlty_columnSummary' (which could include missing value stats), nor does it specify prerequisites or exclusions. This lack of context makes it harder for an AI agent to choose appropriately.

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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