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

Adds rich behavioral context beyond the readOnly/idempotent annotations: results come from a curated table cited to textbooks, papers and library docs, and 'unknown' means the table records nothing and is never guessed. This epistemic-honesty guarantee, plus the note that it returns a 'conditional' verdict with unknowns named or 'none_compatible', is exactly the kind of trait annotations cannot convey.

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?

A single dense paragraph with the core purpose front-loaded and zero filler. It is lengthy because it packs in constraint examples and return semantics, but every clause contributes; only the enumeration of example constraint blobs is slightly heavy.

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 carries the return-value burden and does so fully: winner, 'conditional' with named unknowns, 'none_compatible', per-candidate bounds, when-not advice, and unanswered decisive questions. Combined with the two input modes for zero required params, an agent has everything needed to call and interpret it.

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 coverage is already 100% and the schema enumerates every constraint key, so the baseline is 3. The description goes further by showing how constraints are semantically consumed ('satisfied / violated / unknown per constraint') and by giving worked JSON examples, adding meaning beyond field documentation.

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?

States a specific verb and resource: compare candidate algorithms against the facts of a given instance and return a verdict. It names the two input modes (method names or a problem description) and the output artifacts (satisfied/violated/unknown per constraint), which clearly separates it from siblings like algorithm_find and algorithm_lookup.

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?

Explains how to invoke it (pass names, or a problem description, plus constraints formatted as instance facts) with concrete examples for each constraint style. It does not, however, state when to prefer this over algorithm_lookup or algorithm_find when both could apply, so the boundary with siblings is left implicit.

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