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PipelineFlow

PipelineFlow

by johannschiffer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

list_pipelines

List active pipelines with node and edge metrics so AI agents can inspect workflow graph status and structure.

Instructions

List all active pipelines with their node and edge metrics.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_pipelines"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It does disclose one behavioral trait beyond the name: only *active* pipelines are returned, and node/edge metrics are included. However, it says nothing about ordering, pagination, permissions, or how inactive pipelines are treated.

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?

One short sentence, front-loaded with the action and resource, with the scope qualifier and return content packed in efficiently. No filler whatsoever.

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 trivial parameterless read tool with no annotations and no output schema, the description usefully names the returned fields (node and edge metrics), which compensates for the absent output schema. Minor gaps around ordering/pagination remain but are not critical.

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?

The tool takes zero parameters, so there is no parameter semantics for the description to add or omit; the schema is trivially complete. Baseline for a 0-parameter tool 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+resource ('List all active pipelines') plus the returned payload ('node and edge metrics'), which distinguishes it from the write/execute siblings like create_pipeline and run_pipeline. It never explicitly names a sibling or contrasts with them, so it stops short of a 5.

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 statement of when to use this versus alternatives (e.g., export_pipeline_mermaid for visualization or get_node_output for single-node inspection). The tool's read-only list nature implies its usage, but no condition, prerequisite, or exclusion is given.

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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