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teradata-gcfr-mcp-server

by Pibbers

gcfr_failed_processes

Retrieve failed process instances with error details to quickly diagnose batch failures. Filter by date range, stream key, or process name for targeted investigation.

Instructions

Show failed process instances with full error details.

This is the first tool to use when investigating a batch failure. date_from and date_to default to yesterday and today respectively. Optionally filter by stream_key and/or process_name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_toNo
date_fromNo
stream_keyNo
process_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations present, the description carries the full transparency burden. It conveys a read-only operation ('Show'), discloses default date behavior, and explains optional filtering. It does not cover every edge case such as date format or result limits, but the core behavioral traits are clearly described.

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 four short sentences with no filler. The primary purpose is front-loaded, followed by usage priority, defaults, and filter options—each sentence earns its place.

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 simple read-only tool with four optional parameters and an output schema, the description covers purpose, invocation priority, defaults, and filtering. The main omissions are minor—exact date format and timezone handling—but they do not meaningfully block an agent from selecting and calling this tool.

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?

Schema description coverage is 0%, and the schema only provides names and types. The description compensates by naming all four parameters, stating the defaults for date_from and date_to, and clarifying that stream_key and process_name are optional filters. It could add date-format details, but the essential semantics are present.

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: 'Show failed process instances with full error details.' It also frames this tool as 'the first tool to use when investigating a batch failure,' which clearly distinguishes its role from sibling status, history, and stats tools even without naming them.

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

Usage Guidelines4/5

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

The description explicitly states a concrete use case: investigating a batch failure, and positions this tool as the first one to call. It does not enumerate when not to use it or name alternatives, but the priority guidance is strong and actionable.

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