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Teradata MCP Server

Official
by Teradata
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Python
Remote

Base Tableusage

base_tableUsage
Read-onlyIdempotent

Identify table and view access frequency and per-user query patterns in Teradata, revealing the most queried objects and their users.

Instructions

Report access frequency and per-user query patterns for tables and views in a Teradata database, showing which objects are most actively queried and by whom. Use when the user asks how often tables are accessed, which tables are most popular, or which users are running queries against a database. For discovering which tables appear together in the same queries, use base_tableAffinity instead.

Arguments: database_name - Database name. Leave empty for all databases. persist - If True, materializes result as a volatile table and returns table name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
persistNoIf True, materializes result as a volatile table and returns table name
database_nameNoDatabase name. Leave empty for all databases.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / database_name / anyOf
      Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
    • changedInput schema / properties / database_name / default
      Previous value: -nullNew value: +""
    • addedInput schema / properties / database_name / description
      Added value: +"Database name. Leave empty for all databases."
    • removedInput schema / properties / database_name / title
      Removed value: -"Database Name"
    • addedInput schema / properties / database_name / type
      Added value: +"string"
    • addedInput schema / properties / persist
      Added value: +{
+  "default": false,
+  "description": "If True, materializes result as a volatile table and returns table name",
+  "type": "boolean"
+}
  2. Changed1 schema field changedv1.0.0
    • removedInput schema / title
      Removed value: -"handle_base_tableUsageArguments"
  3. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds the persistence behavior (materializing as a volatile table when persist=True), but this is also present in the input schema, so it adds only modest value beyond structured fields. There is no contradiction.

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 compact and front-loaded: purpose, usage triggers, and alternative are stated in the first two sentences. The Arguments block is redundant with the schema but not bloated, and the overall length is appropriate for a two-parameter tool.

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?

For a read-only reporting tool with two optional parameters and no output schema, the description adequately conveys what is reported (access frequency, per-user patterns, popular objects) and how optional parameters behave. It could describe output shape more concretely, but the coverage is sufficient for correct invocation.

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?

Schema description coverage is 100%, and the input schema already documents both database_name and persist with the same wording. The description repeats those explanations rather than adding new semantic detail, so it meets the baseline but does not elevate it.

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 opens with a specific verb and resource: 'Report access frequency and per-user query patterns for tables and views in a Teradata database.' It also explicitly differentiates itself from the sibling tool base_tableAffinity by noting that co-occurrence analysis belongs to that tool, so an agent can select correctly.

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 gives explicit trigger conditions: 'Use when the user asks how often tables are accessed, which tables are most popular, or which users are running queries against a database.' It also provides a when-not and alternative: 'For discovering which tables appear together in the same queries, use base_tableAffinity instead.'

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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