Skip to main content
Glama
Sign In
k11 API Functional QA

k11 API Functional QA

Official
by K11-Software-Solutions
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Draft flow from a user story

draft_flow

Converts a user story or acceptance criteria into test scenarios, likely API operations, and a flow skeleton with chained IDs plus value and database checks to complete.

Instructions

Turn a user story or acceptance criteria into scenarios, the spec operations most likely involved, and a flow skeleton to complete (chain IDs with save/${...}, add value and db checks).

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": {
      "story": "<story>"
    },
    "name": "draft_flow"
  }
}

Input Schema

NameRequiredDescriptionDefault
specNo
storyYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the return contents (scenarios, spec operations, a skeleton) plus completion hints such as chaining IDs with save/${...} and adding value and db checks. It says nothing about whether anything is persisted, how the spec is resolved, or what happens on ambiguous stories, which are significant gaps for an unannotated generation tool.

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 dense sentence that front-loads the input and enumerates outputs, with no filler. The parenthetical completion hints are useful rather than wasteful, though their scope could be split for readability.

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?

The description substitutes for the absent output schema by listing what is returned and how to finish the skeleton, which is the most important missing piece. It remains incomplete on the 'spec' parameter and on whether output is returned as text or written anywhere, so an agent still has unresolved questions before calling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It only implicitly covers 'story' (user story or acceptance criteria) and never explains the optional 'spec' parameter at all — not its meaning, format, nor the default behavior when omitted.

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 concrete verb-and-output set: it turns a story or acceptance criteria into scenarios, likely spec operations, and a flow skeleton. That distinguishes it from write_flow and run_flows by implying a draft/generation step rather than persistence or execution, though it never names a sibling to contrast against.

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?

Usage is implied rather than stated: 'a flow skeleton to complete' suggests this is a starting point before write_flow, but there is no explicit when-to-use, when-not-to-use, or alternative named. An agent can infer a sensible workflow but must guess about cases like whether it should be used when a full flow already exists.

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

Deploy Server

Other Tools