sparql_ask

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

With no annotations, the description discloses that the tool returns a boolean result and is for yes/no questions, but it does not explicitly mention read-only nature, error handling, or permission requirements. It adds some behavioral context but not comprehensive coverage.

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?

Two sentences, immediately conveys action and purpose with no filler. Each sentence earns its place: one for function, one for use case.

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?

The description covers purpose, return type, and typical use case. Given the simple tool and full schema coverage, it is sufficiently complete, though it could briefly mention repository_id behavior or contrast with SELECT more explicitly.

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?

The input schema already describes both parameters with 100% coverage, so the baseline score of 3 applies. The description adds no additional parameter semantics beyond what the schema provides.

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 'Execute a SPARQL ASK query' and specifies the output 'return a boolean result', distinguishing it from sibling SPARQL tools like sparql_select and sparql_construct. The yes/no phrasing makes the tool's specific role unambiguous.

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?

It explicitly says 'Use this for yes/no questions about the data', providing a clear when-to-use context. It does not explicitly contrast with alternatives, but the yes/no framing differentiates it from SELECT/CONSTRUCT queries enough to guide selection.

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