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Featureflip

Official

Create feature flag

create_flag

Create a feature flag in a project across all environments (initially disabled). Supports Boolean, String, Number, and Json types, with automatic true/false variations for Boolean and optional custom variations for other types.

Instructions

Create a feature flag. type is one of Boolean|String|Number|Json. Boolean flags get true/false variations automatically; for other types pass initialVariations. The flag is created in every environment of the project (disabled).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesUnique flag key, e.g. checkout-v2
nameYes
tagsNo
typeYes
projectYesProject key
descriptionNo
idempotency_keyNoIdempotency-Key header for safe retries
clientSideVisibleNoExpose to client-side SDKs (browser/mobile)
initialVariationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.6

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations, the description discloses important side effects: the flag is created in every environment of the project and starts disabled, and Boolean flags automatically receive true/false variations. This helps the agent anticipate cross-environment impact. It does not cover every possible behavioral aspect such as permissions or return payload, but the core creation behavior is transparent.

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?

The description is three sentences with no filler. It front-loads the primary purpose and then gives only the essential type and variation behavior an agent needs to call the tool correctly.

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 mutation tool with nine parameters and no output schema, the description together with the input schema is largely sufficient: required parameters are in the schema, type-specific rules are explained, and the disabled/every-environment behavior is disclosed. It stops short of describing the response or error scenarios, but those gaps are unlikely to cause incorrect invocation.

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 low at 44%, and the description compensates for the most important parameters by explaining the type enum and the conditional requirement for initialVariations on non-Boolean types. However, it leaves several parameters such as name, description, and tags without additional meaning, and the schema itself only partially covers those.

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 opening sentence states a clear verb and resource: Create a feature flag. The rest of the description adds type-specific detail and creation semantics, distinguishing this creation tool from siblings such as update_flag, delete_flag, and get_flag.

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

Usage Guidelines4/5

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

The description makes obvious that this tool is for creating a new flag rather than updating or deleting one, and it gives clear conditional guidance about when to pass initialVariations versus relying on automatic Boolean variations. It does not explicitly name sibling alternatives or when-not-to-use conditions, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.