Retrieve comprehensive statistics about your loaded ontology, including triple count, class and property counts, and individual counts, to assess its structure and scale.
Get statistics about the loaded ontology (triple count, classes, properties, individuals)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as performance implications, side effects, or limitations. For a statistics tool, it should mention if computation is resource-intensive or if results are cached.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence front-loads the action and outcome. No extraneous words; each element is essential.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple stateless retrieval with no parameters, the description is adequate. However, it does not specify the output format (e.g., JSON structure) or whether it reflects current state after recent changes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has no parameters, so schema coverage is 100%. The description adds value by enumerating the statistics returned (triple count, classes, etc.), which is not evident from the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get statistics about the loaded ontology' and lists example metrics (triple count, classes, properties, individuals). This distinguishes it from sibling tools like onto_load or onto_apply.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly defines when to use this tool (to obtain ontology overview). Although no explicit comparison to siblings, the zero-parameter simplicity and clear outcome make usage obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.