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power-automate-mcp

power-automate-mcp

by OwnOptic
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

list_runs

Read-only

Retrieve a flow's recent runs newest first, filter by status, and get run IDs and timing details to diagnose failures and feed failure explanations.

Instructions

List recent runs for a flow, newest first.

status filter: 'Succeeded', 'Failed', 'Cancelled', 'Running'. Empty for all. Returns run_id, status, start_time, end_time. Feed a failed run_id to explain_run.

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": {
      "flow_id": "<flow_id>"
    },
    "name": "list_runs"
  }
}

Input Schema

NameRequiredDescriptionDefault
topNo
statusNo
flow_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.1.0

TDQS

A3.6/5.0
Behavior4/5

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

The readOnlyHint annotation already establishes the safety profile, so the description's job is lighter. It adds real value beyond the annotations: result ordering ("newest first"), the accepted status values, and the empty-string-means-all convention, none of which are inferable from the schema since there is no enum.

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?

Front-loaded with purpose, then the filter contract, then the handoff to explain_run; every line carries information. The telegraphic fragments ("Empty for all", "status filter:") are efficient but slightly under-framed, which keeps it off a 5.

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?

Because an output schema exists, enumerating return fields is redundant and the real gaps matter more: the 'top' limit and any pagination or truncation behavior are unaddressed. For a simple three-parameter read tool, this is adequate but not fully self-contained.

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 carries the full burden of parameter meaning. It usefully documents the status parameter and its empty default, but says nothing about the 'top' parameter (default 10), leaving the result-limit semantics undocumented.

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 names a specific verb and resource ("List recent runs for a flow") plus an ordering guarantee ("newest first"), which is enough for an agent to select it over get_flow or run_flow. It does not explicitly contrast with compare_runs or analyze_flow_health, which also surface run data, so differentiation from those siblings is only partial.

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

Usage Guidelines3/5

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

It provides a clear follow-on path ("Feed a failed run_id to explain_run"), which implies this tool is the discovery step before diagnosis. However, it never states when not to use it or how it differs from compare_runs, so usage guidance is implied rather than explicit.

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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