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Launchdarkly

by io.github.pipeworx-io
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Ld List Flags

ld_list_flags
Read-onlyIdempotent

Return bounded LaunchDarkly feature flags with keys, variations, tags, maintainers, experiment links, lifecycle state, and optional environment rollout configuration. Answers which features are live, experimental, deprecated, or archived.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "limit": 20,
      "filter": "hasExperiment:true",
      "_apiKey": "your-launchdarkly-reader-token",
      "project_key": "web-app",
      "environment_key": "production"
    },
    "name": "ld_list_flags"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum records, default 20 and maximum 100
queryNo
filterNoLaunchDarkly filter expression such as hasExperiment:true or state:live
offsetNoRecords to skip for pagination
_apiKeyYesLaunchDarkly API access token
project_keyYes
environment_keyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorld, covering the safety profile. The description adds 'bounded' (implying limit/pagination) and that environment rollout config is optional, which is useful, but omits pagination behavior details, rate limits, or what happens on missing project/environment.

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?

Two dense sentences that front-load the return scope and end with the business question answered. No filler, no repetition of the name.

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?

It covers the tool's output fields and read-only nature adequately, but with 7 parameters at 57% coverage and no output schema, it leaves parameter documentation gaps (query, project_key, environment_key semantics) and pagination behavior unexplained. Adequate but incomplete for a multi-parameter list tool.

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 57%; the schema documents limit, filter, and offset while query, project_key, and environment_key are undocumented. The description names environment rollout configuration (tying to environment_key) but adds no syntax, default, or format details for query or project_key, so it only marginally compensates for the schema gap.

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?

States a specific verb (Return/List) and resource (LaunchDarkly feature flags) with the exact fields returned enumerated and the question it answers ('which features are live, experimental, deprecated, or archived'). This clearly distinguishes it from siblings like ld_get_flag (single flag) and ld_list_projects/environments.

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 when-to-use or when-not-to-use guidance. It does not explain the distinction from ld_get_flag or tell the agent when to prefer this over newer APIs like ld_list_environments. An agent must infer usage from the name alone.

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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