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Reality Graph Verification Tools

by dev.realitygraph

Lint a task specification

lint_task_spec
Read-only

Check whether a free-text work order for an AI coding agent is verifiable BEFORE handing it over. Heuristic, deterministic lint of the task's form against the four building blocks of a checkable task (goal, boundaries, acceptance criteria, validation plan) plus rule checks (vague adjectives without numbers, unnamed unhappy paths, missing file anchors). Returns a status table with evidence, the concrete questions that close each gap, and a fill-in skeleton. It checks form, not content — no LLM, nothing stored. Set lang='de' for a German report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoReport language (default: en)
taskYesThe work order / task text you intend to give an AI coding agent (English or German)

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=true), the description clarifies it does not use LLM, stores nothing, and returns a status table with evidence and questions. This fully discloses behavioral traits.

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?

Four sentences: purpose, methodology, output, language option. Front-loaded with the key action, no redundant words, and each sentence adds necessary information.

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

Completeness5/5

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

For a simple tool with two parameters and no output schema, the description covers purpose, method, output format, and limitations (no LLM, no storage), making it self-contained.

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

Parameters4/5

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

Both parameters have schema descriptions (100% coverage), but the description adds a usage hint for lang ('Set lang='de' for a German report'), providing extra guidance beyond the enum.

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 description clearly states the tool's purpose: checking if a free-text work order for an AI coding agent is verifiable, using heuristic linting against four building blocks. This distinguishes it from sibling tools like validate_task_contract which likely perform deeper verification.

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 implies use before handing over a task to an AI agent, and notes it checks form not content. However, it doesn't explicitly exclude cases or compare with siblings like calculate_verification_capacity, leaving some ambiguity.

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

All Reality Graph Verification Tools toolsInspect
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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, ranging from capacity planning to template generation and validation. There is no functional overlap; even the two template tools (get_task_contract_template and get_verification_report_template) serve different artifacts.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using lowercase with underscores. Verbs like calculate, check, fetch, get, lint, plan, search, and validate clearly indicate actions, and the nouns are specific and singular.

Tool Count5/5

With 10 tools, the surface is well-scoped for the domain of AI coding verification. Each tool addresses a specific task without redundancy, and the count feels appropriate for a focused but complete tool suite.

Completeness5/5

The tool set covers the full lifecycle of verification: planning (plan_change_verification), specification (get_task_contract_template, lint_task_spec, validate_task_contract), execution (check_release_readiness, check_verification_debt, calculate_verification_capacity), and reporting (get_verification_report_template, fetch, search). No obvious gaps are evident.

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