get_plant_problem_profileGet a structured public aquatic plant problem or deficiency profile using the exact slug returned by search_plant_problems.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Exact single-segment slug returned by the related Atlarium search tool. | |
| language | No | Optional preferred response language: it, en or es. |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| tool | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is fully covered. The description adds the fact that the profile is 'public', implying no authentication is needed, but this is a minor addition. No contradictions with annotations.
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 a single sentence that immediately states the core action and resource, then adds the key usage condition. No redundant words, highly efficient.
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 presence of an output schema, annotations, and 100% parameter coverage, the description provides all necessary context: what it does (get profile), what input is required (slug from search), and an optional customization (language). No gaps remain for the agent to infer.
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% and both parameters are well-documented in the schema. The description reinforces the coupling between the slug and search_plant_problems, adding practical semantics beyond the schema's 'related Atlarium search tool' phrasing. This extra context justifies a score above baseline 3.
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 clearly states the verb 'Get' and the resource 'structured public aquatic plant problem or deficiency profile', and it explicitly links the input slug to the sibling tool 'search_plant_problems', distinguishing it from other get tools like get_algae_profile or get_disease_profile.
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 provides explicit guidance on when to use the tool: only after obtaining the exact slug from search_plant_problems. It does not mention when not to use it or list alternatives, but the prerequisite is clearly stated, making it easy for the agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a unique name and clearly documented purpose. The 'get_' and 'search_' pairs are explicitly linked via slugs, and the 'calculate_', 'check_', 'convert_', 'generate_', 'list_', 'match_', and 'suggest_' tools all address distinct operations. There is no ambiguity or overlap.
All 39 tools follow a consistent verb_noun pattern in snake_case (e.g., calculate_tank_volume, search_plants, get_fish_profile). No mixing of conventions or non-standard naming is present.
With 39 tools, the set is far above the typical well-scoped range of 3-15. While the domain is broad, the sheer number makes the tool surface heavy and potentially overwhelming for an agent to navigate effectively.
The tool set covers the entire domain of an aquarium habitat database: calculations, unit conversion, species compatibility, profile retrieval for all entity types, diagnostic matching, and habitat suggestion. No obvious gaps exist for a read-only advisory API.