derivation_suggest_next

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the key algorithmic heuristic (higher score when a candidate defines a symbol in current_expression and matches the goal) and notes the output is ordered best-first. It stops short of edge-case behavior (e.g., empty candidate list), but for a ranking tool this is solid transparency.

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?

The description is front-loaded with the main purpose, then uses a clear Args/Returns structure. It is moderately sized but every sentence contributes useful information—the retrieval context, scoring rule, and return shape. Slightly dense but not wasteful.

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?

Given the tool's moderate complexity (3 params, no annotations, but an output schema), the description is complete enough: it explains the input sources, the ranking rationale, and the output format. It lacks details about possible values for 'kind' or handling of empty inputs, but these are marginal for an agent's invocation decision.

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 provides no parameter descriptions (0% coverage), so the description must compensate. It defines all three parameters in the Args section, including the nested structure of 'candidates' with optional fields like 'expression', 'kind', 'provides', and explains their role as formulas/modifications/operations. This goes well beyond the bare schema.

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 opens with a specific verb 'Rank' and a clear resource 'candidate next steps for a derivation', and immediately states the ranking criterion 'by relevance'. This distinguishes it from sibling tools that actually perform steps (e.g., derivation_substitute) or retrieve formulas (formula_search), making the tool's purpose 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?

The description explicitly tells the user to supply candidates retrieved from open sources like formula_search, and explains the scoring logic, which clarifies when to use this tool. It does not explicitly name alternative tools to avoid using, but the context ('retrieval-augmented' versus actually applying steps) is clear enough for an agent to decide.

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