Discover available tools by category or price without loading all schemas. Start here to save tokens. (FREE)
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Search query to filter tools by name or description | |
| category | No | Filter by category | all |
| max_price_usd | No | Max price per call in USD (0 = free only) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds useful context beyond annotations: it is '(FREE)' and it avoids loading all schemas, which signals low cost and lightweight behavior. 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, concise sentence with a parenthetical note. It is front-loaded with the core purpose and includes only high-value information (token savings, free). No wasted words.
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 simplicity (three optional params, no output schema), the description adequately covers its role as a discovery tool. It explains why to use it (saves tokens) and what it does (finds tools by category/price). It doesn't describe return format, but for a meta-tool used to browse the catalog, this is sufficient.
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 description coverage is 100%, with each parameter (query, category, max_price_usd) already documented. The description adds minimal extra meaning by referencing 'by category or price', which maps to category and max_price_usd, but this is redundant with the schema. Baseline 3 is appropriate.
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 tool's function: 'Discover available tools by category or price'. This uses a specific verb ('Discover') and resource ('available tools'), and distinguishes it from siblings like search_artworks (which searches artworks) and get_tool_schema (which loads a specific schema).
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 gives a clear usage directive: 'Start here to save tokens', implying this should be the first tool used before loading individual schemas. It also contrasts with loading all schemas, indicating when it is useful, though it doesn't explicitly mention when not to use it or name alternatives.
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.
Most tools have distinct purposes, but there is some overlap between get_artwork and get_artwork_oracle, which both retrieve artwork metadata with different detail levels, potentially causing confusion. Other tools like enrich_metadata and infuse_metadata also have related but distinct functions, but descriptions help clarify differences.
Tool names generally follow a consistent verb_noun pattern (e.g., check_balance, delete_asset, resize_image), with minor deviations like mockup_image (noun_verb) and get_artwork_oracle (longer compound name). Overall, the naming is readable and predictable, though not perfectly uniform.
With 27 tools, the count is borderline high for a single server, as it covers a broad range of functionalities from artwork retrieval to image processing and compliance. While each tool seems useful, the scope feels heavy and could overwhelm agents, suggesting it might be better split into more focused servers.
The tool set provides comprehensive coverage for digital asset management, artwork analysis, and image processing, including CRUD operations (save_asset, get_asset, list_assets, delete_asset), metadata enrichment, compliance, and various image utilities. No obvious gaps are present for the stated domain.