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validate_ontology

Audit ontology data integrity by detecting broken links, orphan entities, and association breaks. Optionally auto-repair fixable issues like orphan entity markers.

Instructions

检查本体数据完整性——实体数/关联断裂/孤儿实体/死链。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fixNo是否自动修复可修复的问题(如孤儿实体标记),默认 false

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only states 'check', which implies a read-only operation, but fails to disclose that the tool can modify data when the 'fix' parameter is set to true. This is a significant omission for an agent evaluating side effects, especially since the parameter explicitly allows fixing issues. The description also does not mention result format or error 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?

The description is a single, concise sentence that front-loads the core action and scope. Every word contributes meaning, and there is no redundancy or filler. It is appropriately sized for the tool's simplicity.

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

Completeness3/5

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

Given the tool's simplicity and the complete parameter schema, the description is adequate but not fully complete. It does not explain what the output looks like (e.g., a report or counts), nor does it mention the optional fixing behavior, which is a significant behavioral aspect. With no output schema or annotations, the description should cover these gaps to be fully helpful for an agent.

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

Parameters3/5

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

The input schema fully documents the single 'fix' parameter with a clear description, so schema coverage is 100%. The tool description adds no additional parameter context beyond what the schema provides, so it meets the baseline of 3 without exceeding it.

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 clearly states the verb 'check' and the resource 'ontology data', and enumerates specific aspects checked (entity count, broken links, orphan entities, dead links). It is specific enough to convey the tool's purpose, though it does not explicitly differentiate from sibling audit tools like run_audit or audit_data_change, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used when ontology integrity needs verification, but it gives no explicit guidance on when to choose this over alternatives, nor any exclusions or prerequisites. The existence of the 'fix' parameter suggests usage scenarios, but it is not mentioned in the description itself, leaving the agent to infer.

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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