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teradata-gcfr-mcp-server

by Pibbers

gcfr_sla_process_report

Compare expected and actual process timings to identify SLA misses. Returns duration as formatted interval, with date range defaulting to yesterday and today.

Instructions

Compare expected vs actual process start time, end time, and duration.

Shows whether SLAs were met. SLA_Run_Duration is returned as a formatted string (INTERVAL DAY TO SECOND). date_from and date_to default to yesterday and today respectively.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_toNo
date_fromNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that SLA_Run_Duration is returned as a formatted INTERVAL string and that date parameters have defaults. However, it does not mention whether the operation is read-only, potential errors, or permissions. For a report tool, the output-format note adds value, but the lack of explicit safety or error behavior prevents a higher score.

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 three sentences, each serving a purpose: the primary function, a key output detail, and parameter defaults. It is front-loaded with the main purpose and avoids redundancy. It could be slightly more compact but is well-structured for a small tool.

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

Completeness3/5

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

The description covers the main functionality, a specific output format nuance, and default parameter behavior. It does not explain how to interpret the comparison results or mention any limitations (e.g., process-only scope). With an output schema present, return values are partially covered, but the lack of usage context and edge cases leaves some gaps for an agent.

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 0%, so the description must compensate. It provides useful default behavior for date_from and date_to (yesterday and today), which is not in the schema. However, it does not specify the expected date format or how to pass custom values, leaving the agent to infer from the string type. This partial compensation justifies a middle score.

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 function: comparing expected vs actual process start/end times and duration to show SLA compliance. It identifies a specific resource (process SLA) and a clear outcome, but does not explicitly differentiate from the sibling gcfr_sla_stream_report, which is the stream counterpart.

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 gives no guidance on when to use this tool versus alternatives like gcfr_process_history or gcfr_sla_stream_report. It only mentions the default date range, implying it is a reporting tool, but it does not state explicit conditions for selection or exclusions.

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