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mcp-sac-tools

mcp-sac-tools

by pmankineni
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

sac_import_invalid_rows

Retrieve invalid or rejected rows from a validated SAC data import job to identify and correct data issues.

Instructions

Get the invalid/rejected rows from a validated import job.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "modelId": "<modelId>",
      "jobId": "<jobId>"
    },
    "name": "sac_import_invalid_rows"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobIdYesThe job ID
modelIdYesThe model/provider ID
dataTypeNo
dimensionNameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It correctly indicates a read operation but provides no details on side effects, permissions, or constraints (e.g., job must be validated). This is adequate but not thorough.

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?

Single sentence, no fluff, directly communicates the tool's purpose.

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 import workflow context (many sibling import tools), the description is too brief. It lacks information about return format, pagination, relationship to other import steps, and how to interpret results. No output schema available to compensate.

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 coverage is 50% (jobId and modelId described, dataType and dimensionName not). The description adds no parameter information beyond the schema, leaving the undocumented parameters ambiguous.

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 the action ('Get') and the specific resource ('invalid/rejected rows from a validated import job'), which distinguishes it from sibling import tools like sac_import_status or sac_validate_import.

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?

No guidance on when to use this tool versus alternatives (e.g., after validation, before running import). The phrase 'from a validated import job' implies a prerequisite but doesn't explicitly state it, nor does it mention any exclusion conditions.

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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