arquimedes_sample_problems

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered; the description still adds real behavioral context beyond them: results are summaries without solutions, sampling is uniform over the filtered pool, and the same seed reproduces a draw (consistent with, not contradicting, idempotentHint=false since unseeded draws vary). It doesn't state pagination or performance characteristics, keeping it short of a 5.

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?

Four tightly packed sentences with no filler: capability and filters first, then use cases, then return shape, then the disambiguation pointer to a sibling. Every sentence carries distinct information and the routing-critical content is front-loaded.

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?

With no output schema, the description steps in and explains what comes back (summaries without solutions) and how to get the full content (pass an id to arquimedes_get_problem). Combined with the filter list and seed semantics, an agent has everything needed to call this correctly without guessing.

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?

Schema description coverage is 100%, so every parameter is already documented, and the description's filter list (category, dataset, competition, year, Mohs range, multiple-choice, diagram) largely restates the schema. It adds only marginal semantics, such as the Mohs range being 1.0–10.0 and the seed's repeatability, which the schema field descriptions already convey. Baseline 3 applies.

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?

Names a specific verb and resource ('Draw random problems') plus the exact scale of the corpus (1,038,963 competition/olympiad problems) and the sampling rule (uniform among matching filters). It also distinguishes itself from both siblings by naming arquimedes_get_problem and arquimedes_search_problems, so an agent can route without opening a schema.

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?

Gives explicit use cases ('build practice sets, mock contests, or evaluation samples') and explicit exclusions with named alternatives: use arquimedes_search_problems for topic/keyword lookup, use arquimedes_get_problem for solutions. The when-to-use and when-not-to-use conditions are both spelled out.

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