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Reality Graph Verification Tools

by dev.realitygraph

Get the verification report template

get_verification_report_template
Read-only

Returns the free fill-in template (v0) for a verification report — the artifact you write right after an AI-assisted run: task recap, files changed AND files confirmed untouched, validation results per acceptance criterion (not authored by the generating model), what was skipped, limitations, and the explicit decision. format='json' for a machine-fillable structure; default is a compact markdown file. Static content, nothing stored. lang='de' for German.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage (default: en)
formatNoTemplate format (default: markdown)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds that content is static and nothing is stored, confirming safety and no side effects. This adds context beyond the annotations.

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?

The description is a single dense sentence but contains all essential information. It is front-loaded with the main purpose. Could be slightly more structured but remains efficient and clear.

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?

Given no output schema, the description details the template contents and behavior. It covers format options, language, and confirms static nature. The tool is simple with 0 required params, and the description is sufficiently complete.

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?

Schema coverage is 100% with enum descriptions. The description enriches parameters: explains format='json' for machine-fillable vs default markdown, and lang='de' for German. This adds value beyond the raw schema.

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 returns a verification report template, explains its contents (task recap, files changed/touched, validation results, etc.), and distinguishes it from sibling tools like get_task_contract_template. The verb 'Returns' and resource 'template for verification report' are specific.

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 indicates when to use it ('right after an AI-assisted run') and explains format options ('json' for machine-fillable, default markdown). It does not explicitly state when not to use it or compare to siblings, but the context is clear enough.

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

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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.

Resources