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Reality Graph Verification Tools

by dev.realitygraph

Validate a filled task contract

validate_task_contract
Read-only

Deterministically validates a FILLED task contract (the JSON structure from get_task_contract_template): completeness of goal/non-goals/boundaries, decidability of each acceptance criterion (vague words, missing measurable markers), automated checks in the validation plan, expected evidence, and leftover placeholders. Returns a verdict (PASS / PASS WITH WARNINGS / FAIL), four dimension scores, and a concrete fix per finding. Validates form and completeness, not correctness. No LLM, nothing stored. lang='de' for German.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoReport language (default: en)
contractYesThe filled task contract as a JSON string (structure from get_task_contract_template, format='json')

TDQS

A4.6/5.0
Behavior5/5

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

Annotations indicate read-only and non-destructive behavior. The description adds valuable context: deterministic validation, no LLM involvement, no storage, and detailed checks (vague words, missing markers). No contradictions.

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 concise at ~100 words, front-loaded with key verb 'Validates', and each sentence provides essential information without redundancy.

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 having no output schema, the description fully covers return values (verdict, scores, fixes). With simple parameters and high schema coverage, the description is complete and enables correct invocation.

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%, so baseline 3. Description adds meaning: contract must be filled JSON from get_task_contract_template, and explains lang enum values ('de' for German), improving parameter understanding.

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 validates a filled task contract for completeness, decidability, and other checks, listing specific validation dimensions and outputs. It distinguishes itself from siblings by noting it validates form, not correctness, and uses no LLM or storage.

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 explains when to use this tool (after filling a task contract template) and what it does not do (no LLM, no storage, validates form only). It lacks explicit when-not-to-use alternatives but context is sufficient.

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.

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