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Reality Graph Verification Tools

by dev.realitygraph

Get the verifiable task contract template

get_task_contract_template
Read-only

Returns Reality Graph's free fill-in template (v0) for a verifiable task contract: goal, non-goals, boundaries (may change / must not change / forbidden), 3-7 yes/no acceptance criteria, validation plan, expected evidence, assumptions, open questions — with a filled example and fill-in guidance. Write the contract before an AI agent runs; verify the result against it after. format='json' returns a machine-fillable JSON structure; default is a compact markdown skeleton. Set lang='de' for German. Static content, nothing stored.

Input Schema

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

TDQS

A4.7/5.0
Behavior5/5

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

The description reinforces annotations: 'Static content, nothing stored' aligns with readOnlyHint=true and destructiveHint=false. It also details the template structure, format options, and language support, providing full behavioral transparency 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph with front-loaded purpose, followed by key details. Every sentence contributes value—no redundancy. It efficiently covers the template structure, usage context, format options, and static nature.

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?

Despite no output schema, the description comprehensively explains the template contents, including filled example and guidance. It covers all significant aspects: format variants, language support, and static behavior. This is sufficient for the tool's purpose.

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?

With 100% schema coverage and enum descriptions for both parameters, the schema already provides clear definitions. The description adds meaningful context: format='json' returns machine-fillable JSON, default is markdown; lang='de' sets German. This adds value beyond the 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 it returns a template for a verifiable task contract, explicitly listing its sections (goal, non-goals, etc.). It distinguishes itself from sibling tools like validate_task_contract by explaining the usage sequence: write the contract before running an AI agent, and verify after.

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 gives clear usage context: 'Write the contract before an AI agent runs; verify the result against it after.' It implies alternatives (e.g., validate_task_contract for verification) but does not explicitly name other siblings or state when not to use this tool.

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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