Get the AI planner templates

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

Annotations already declare this a safe read (readOnlyHint=true, destructiveHint=false), but the description goes well beyond: it discloses the credit cost model, contrasts image vs. non-image template pricing with concrete figures, and reveals an environment-level constraint (PLANVORTEX_MCP_ALLOW_AI=1) that explains if and why plan creation is unavailable. This is context an agent cannot obtain from annotations or schema.

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 core purpose and the 'read this before proposing a plan' instruction are front-loaded, and each paragraph carries distinct information (templates, pricing, env gating). It is dense with parentheses and asides, but every sentence contributes necessary facts rather than padding.

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 must describe the return shape, and it does: five named templates, their source types, the fields involved, and the credit cost per template. Combined with the safety annotations and the env-gating explanation, an agent has everything needed to call and interpret this tool.

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 tool takes zero parameters, so per the rubric the baseline is 4. The description correctly adds no parameter guidance because there is nothing to parameterize; the five template names it lists are return values, not inputs.

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 resource (the AI planner templates) and its payload content (the five template types and the credits each costs), enumerating them by name so the agent knows exactly what comes back. It is clearly distinguishable from siblings like list_ai_plans or get_ai_plan, which describe existing plans rather than the generation sources.

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?

Explicitly instructs when to call it: 'Read this before proposing a plan', because costs and fields must not be guessed. It also explains the gating condition for create_ai_plan and tells the agent what to do when that sibling is absent from its tool list, which is direct routing guidance.

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