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ncloud-mcp-server

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TypeScript
Remote

ncloud_dataflow_get_execution_result

Read-only

Retrieve Data Flow job execution statistics for a date range. Use execution, success, and failure counts to track dashboard performance and diagnose failures.

Instructions

Get job execution result statistics (execution count, success count, failure count) for Data Flow dashboard.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endDateYesEnd of the query range (ISO 8601, e.g. 2025-03-20T23:59:59) (required)
startDateYesStart of the query range (ISO 8601, e.g. 2025-03-19T00:00:00) (required)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv2.0.1
    • addedInput schema / properties / endDate
      Added value: +{
+  "description": "End of the query range (ISO 8601, e.g. 2025-03-20T23:59:59) (required)",
+  "type": "string"
+}
    • removedInput schema / properties / endTime
      Removed value: -{
-  "description": "End time for query range (ISO 8601 format)",
-  "type": "string"
-}
    • addedInput schema / properties / startDate
      Added value: +{
+  "description": "Start of the query range (ISO 8601, e.g. 2025-03-19T00:00:00) (required)",
+  "type": "string"
+}
    • removedInput schema / properties / startTime
      Removed value: -{
-  "description": "Start time for query range (ISO 8601 format)",
-  "type": "string"
-}
    • addedInput schema / required
      Added value: +[
+  "startDate",
+  "endDate"
+]
  2. First observedv1.10.1

TDQS

B3.2/5.0
Behavior3/5

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

readOnlyHint=true already establishes the safe read-only nature, so the bar is lower. The description adds value by disclosing the return shape (execution/success/failure counts), but omits any auth, rate-limit, or aggregation-window behavior beyond the date range.

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?

A single, front-loaded sentence that names the tool's action first and the returned fields second. No filler or redundancy, though it is arguably a touch terse for a metrics 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?

With no output schema, the description carries the burden of describing return content and does so by listing the three count fields, which is exactly what an agent needs to know. Minor gaps remain around scope (per-workflow vs dashboard-wide) but nothing critical is missing for a read-only stats call.

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%, so both startDate and endDate (with ISO 8601 examples and required markers) are fully documented in the schema. The description adds no parameter-level syntax or constraint information, so the baseline of 3 applies.

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?

States a specific verb ('Get') and resource ('job execution result statistics') and even enumerates the returned counts, so the agent knows exactly what comes back. However, it does not distinguish itself from near-named siblings like ncloud_dataflow_get_execution_interval, ncloud_dataflow_get_execution_times, or ncloud_dataflow_get_workflow_executions, leaving ambiguity about which 'execution' tool to pick.

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?

There is no when-to-use or when-not-to-use guidance and no alternative tool is named. An agent facing several similarly named Data Flow execution tools gets no routing help.

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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