Skip to main content
Glama

plan_feature

Analyze feature requirements and generate a detailed plan with user stories, acceptance criteria, and success metrics for development.

Instructions

Product Manager analyzes requirements and creates a detailed feature plan with user stories, acceptance criteria, and success metrics

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoAdditional context about the project or constraints
priorityNoFeature priority level
requirementYesThe feature requirement or user request (min 10 characters)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the shape of the generated content (user stories, acceptance criteria, success metrics), which is useful, but says nothing about whether anything is persisted, whether the result is deterministic, what permissions are needed, or whether it mutates project state.

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 front-loaded sentence with no filler or redundancy. It is efficient, though the brevity comes at the cost of omitting workflow routing that would make the sentence more actionable.

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?

With no output schema, the description partly compensates by naming the deliverables of the plan. However, for a workflow tool sitting among design_architecture, implement_code, and full_workflow siblings, the absence of sequencing or scope guidance leaves the agent guessing when to invoke it.

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 coverage is 100% with clear per-parameter descriptions and an enum for priority, so the schema already does the heavy lifting. The description adds no parameter-level meaning (e.g., how 'context' or 'priority' influence the generated plan), so the baseline of 3 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?

The description states a specific actor (Product Manager), verb (analyzes, creates), and resource (detailed feature plan), plus the artifacts produced (user stories, acceptance criteria, success metrics). This distinguishes it reasonably from siblings like design_architecture or implement_code, though it never names an alternative explicitly.

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 when-to-use guidance: nothing says whether this runs before design_architecture, whether it is a prerequisite for implement_code, or what input qualifies. The agent must infer placement in the workflow entirely from the name.

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