Server Quality Checklist

Tool Scores

• inspectA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description is the sole source of behavioral transparency. It discloses that the output is 'compact JSON' containing specific fields (queryable URI, labels, definitions, direct parent/child classes, direct OWL restrictions), and frames it as a 'local graph neighborhood'. However, it does not explain the effect of the 'limit' parameter or error/edge-case 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 three sentences: purpose, usage scenario, and rationale. Every sentence adds value, with technical details packed into the second sentence while keeping the overall text short.

  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?

  The tool is simple with only two parameters, and an output schema exists, so the return structure is already specified. The description adds usage context and output content expectations, but the unidentified 'limit' parameter leaves a minor gap.

  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?

  Schema coverage is 0%, so the description must fully document parameters. It provides an example identifier ('fibo-sec-eq-eq:Share') and implies it is a compact URI, which helps with the required 'identifier' parameter. But the optional 'limit' parameter is never mentioned, leaving its semantics undocumented.

  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 opens with 'Inspect one FIBO class/entity as an LLM-friendly local graph neighborhood', clearly identifying the verb (inspect), resource (FIBO class/entity), and scope (local graph neighborhood). This differentiates it from the sibling 'sparql' tool, which is a more generic query interface.

  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?

  It provides explicit usage context: 'Use this after discovering a compact URI such as fibo-sec-eq-eq:Share.' It also explains why this tool is preferable ('usually better than asking for only a bare URI'), but it does not explicitly mention when to avoid it or name alternatives, just compares to a bare URI approach.

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

• sparqlA
  4.8/5.0
  Behavior5/5

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

  With no annotations, the description carries the full transparency burden and delivers ample behavioral detail. It discloses the return format ('Returns compact JSON + BM25 suggestions'), server-specific behavior for URI prefix handling, and the distinction that FIBO is a reasoning scaffold rather than a probability source. It also covers coverage gaps, which is non-obvious behavioral context that helps set expectations.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness3/5

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

  The description is extremely long and contains extensive sections like 'THREE-STAGE SYMBOLIC REASONING' and detailed query templates. While well-structured and front-loaded, not every sentence earns its place; some content (e.g., the reasoning methodology) is more of an AI tutorial than essential tool documentation. It is effective but verbose, making it a 3 rather than a 4.

  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?

  Given the tool's complexity—a SPARQL endpoint for a specialized ontology—the description is remarkably complete. It covers purpose, usage rules, return format, URI conventions, coverage gaps, term mappings, and query templates, and even adds an output schema. There is little left to the imagination, making it self-sufficient for correct invocation.

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

  Parameters5/5

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

  The schema only defines a single 'query' string with no description (0% coverage). The description compensates extravagantly by clarifying that the parameter is a SPARQL query, listing built-in prefixes, providing multiple query templates, and explaining how to handle FIBO URIs. This transforms an otherwise opaque parameter into a fully specified interface.

  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 opens with 'Query FIBO - the financial industry ontology...' using a specific verb and resource. It clearly distinguishes itself from the sibling tool 'inspect' by stating 'For one entity, use inspect(uri) to fetch its local semantic neighborhood in one call,' making the tool's scope unmistakable.

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

  Usage Guidelines5/5

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

  The description provides an explicit 'ALWAYS use this tool when:' list with four concrete scenarios, and a 'DO NOT use for: probabilistic inference' exclusion. It also names the alternative tool 'inspect' and lists coverage gaps where the agent should rely on its own knowledge, giving clear when-to-use and when-not-to-use guidance.

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