Find AI Tool Pipeline (Input to Output)

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

Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=true. The description adds genuinely non-redundant behavior: the result is deterministically computed against tagged compatibility data, and multiple ranked pipelines are returned scored on tool quality and path length. It stops short of stating failure behavior when no path exists.

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?

Purpose is front-loaded in the first sentence and the rest is largely earning its place by establishing trust in the result. The parenthetical about the PostgreSQL recursive engine is implementation detail of marginal value, and the trailing content-type list duplicates the enum, costing a bit of tightness.

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 an output schema present and annotations covering the safety profile, the description supplies what is missing: determinism, ranking criteria, and the shape of the answer (multiple scored alternatives). Only edge cases such as empty-result handling and latency/caching are unaddressed.

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 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it enumerates the valid content types for start/goal and explains the practical consequence of the step-count parameter ('compare a fast 2-step option against a more thorough 4-step one'). It says nothing extra about free_only or alternatives.

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 ('find a sequence of AI tools that gets you from one type of content to another') with concrete input/output examples (audio→blog post, image→video). It also implicitly distinguishes itself from siblings like get_workflow_template and get_ai_workflow by insisting the result is 'not a guess, a template, or an LLM improvising' but a structurally verified chain.

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 makes clear when this tool applies: when you need a tool chain bridging one content type to another, with a fast-vs-thorough trade-off. It gestures at alternatives ('not a template') but never names a sibling tool or states an explicit when-not-to-use condition, so routing still requires inference.

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