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inite-ai

ideaudit-tools

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by inite-ai

compute_build_complexity

Compute a build complexity penalty score (0-10) with factor breakdown from external APIs, integrations, and stack tags, highlighting high-impact complexity drivers to support build feasibility audits.

Instructions

Compute build_complexity_penalty (0-10, higher = worse) + per-factor breakdown. Hard tags: ml/realtime/blockchain/hardware/compliance/custom-ai/regulated/on-device-ai/iot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
externalApisCountYes
integrationsCountNo
stackComplexityTagsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It does communicate the output nature (scale, direction, breakdown) and lists the hard tags, which are likely behavioral triggers. However, it does not explicitly state whether the operation is read-only, what a hard tag actually does to the score, or edge-case behavior.

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 sentences with no filler. The first sentence front-loads the output and scale; the second gives a compact, relevant list of hard tags. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 0% schema coverage, no annotations, and no output schema, the description is insufficient for confident invocation. It omits the semantics of two parameters, does not explain how hard tags are applied, and provides no routing context. An agent would need additional reasoning to call this tool correctly.

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 adds meaning only for stackComplexityTags by listing the hard tags, but externalApisCount and integrationsCount are completely undocumented in both the schema and the description. An agent cannot know how those numeric parameters affect the penalty.

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 explicitly states the tool computes a build_complexity_penalty with a 0-10 scale, higher = worse, plus a per-factor breakdown. This is a specific verb+resource+output and leaves no doubt about the tool's core function. It does not explicitly differentiate from sibling compute_* tools, but the metric and hard-tag list make the purpose clear.

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 guidance is provided for when to use this tool versus any of the many compute_* siblings. There is no mention of alternatives, conditions, or context in which build complexity should be computed rather than other metrics. The agent is left to infer routing solely from the tool name.

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