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manzoor-source

Teradata MCP Server

base_readQuery

Read-onlyIdempotent

Execute provided SQL queries against Teradata and return results, with optional persistence as a table for large datasets.

Instructions

Execute a user-provided SQL query against Teradata and return the results. Use this tool ONLY when the user supplies an explicit SQL statement or a request that includes filter conditions (WHERE clause, aggregations, JOINs, etc.). Do NOT use for simply browsing or sampling rows from a table — use base_tablePreview for that. The sql parameter is required and must contain the full SQL text.

Arguments: sql - SQL text, with optional bind-parameter placeholders persist - Set to True to persist the results as a table and reuse it later. Recommended for large result sets. row_limit - Maximum rows to return (default 1000, ceiling 50000). Pass a higher value when you need more rows.

When the response metadata contains 'truncated: true', more rows exist beyond the limit. To get more data:

  • Pass a higher row_limit (up to 50000) to retrieve more rows in the response.

  • Use persist=true to write all rows to a volatile table and query it directly — this bypasses the row limit entirely and is the recommended approach for large result sets.

Returns: ResponseType: formatted response with query results + metadata (includes 'volatile_table' field in metadata if persist=True) (includes 'truncated' and 'row_limit' in metadata when results are capped)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlNo
persistNo
row_limitNo
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds value by explaining behavior on truncation (truncated flag, how to get more rows), the effect of persist (volatile table), and row limit details. It does not contradict annotations.

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 well-structured with clear sections and front-loaded purpose. While it is relatively long, every sentence adds value. Minor redundancy (e.g., repeating truncation handling) could be trimmed, but overall it is efficient and organized.

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

Completeness5/5

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

Given the tool's complexity (SQL execution) and that the schema has no descriptions and no output schema, the description covers all critical aspects: parameter semantics, return metadata (truncated, row_limit, volatile_table), and guidance for large result sets. It is complete for an AI agent to invoke correctly.

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

Parameters5/5

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

Despite the schema having 0% description coverage, the description fully explains all three parameters: sql (required, full SQL text), persist (boolean, recommended for large results), and row_limit (default 1000, ceiling 50000). It adds essential meaning beyond the schema.

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 it executes a user-provided SQL query against Teradata and returns results. It explicitly distinguishes from base_tablePreview by saying not to use for browsing/sampling rows, making the purpose unambiguous and well-differentiated.

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 explicitly states when to use (when user supplies explicit SQL statement or filter conditions) and when not to (use base_tablePreview for browsing/sampling). It also provides guidance on persist and row_limit parameters, giving clear context for tool selection.

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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