random_by_categoryReturn a random food image URL for a specific category; valid categories: biryani, burger, butter-chicken, dessert, dosa, idly, pasta, pizza, rice, samosa.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| image | No | URL to a food image from the specified category |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states the tool returns a 'random' image URL, implying non-deterministic results. This directly contradicts the annotation 'idempotentHint: true', which indicates the tool should be idempotent (same input yields same output). This is a clear annotation contradiction.
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, front-loaded sentence that efficiently conveys the tool's purpose and valid categories. Every word adds value with no redundancy or unnecessary detail.
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 low complexity (one parameter with enum, output schema exists, strong annotations), the description is complete: it states the action, input, output (URL), and valid values. The annotations cover safety and idempotency (despite the contradiction in behavior), and the description adds the remaining context.
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 description lists valid categories, but this merely repeats the enum values already in the input schema. Since schema coverage is 0%, the description should add new meaning, but it does not. It provides no additional context about the parameter's format, constraints, or behavior.
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 'Return' and the resource 'random food image URL for a specific category', distinguishing it from sibling 'random' which likely returns non-category-specific results. The list of valid categories further clarifies scope.
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 says 'for a specific category' and lists valid categories, giving clear usage context. However, it does not mention when not to use this tool or compare with alternatives like the sibling 'random' tool.
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.
Every tool has a clearly distinct purpose, with detailed descriptions that differentiate between similar-sounding tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. The Polymarket-related tools each focus on a specific aspect (arbitrage, edges, tracking, fill risk, cross-venue spread), and memory/subscription tools are neatly separated.
Most tool names follow a verb_noun or noun_verb pattern with underscores (e.g., ask_pipeworx, validate_claim, resolve_entity). However, there is some inconsistency: single-word names like 'forget' and 'random' mix with multi-word names, and a few names use different structures (e.g., bet_research as noun_noun, random_by_category as adjective_preposition).
32 tools is on the high side for a single server, covering a broad range of functionalities from data queries to betting and memory. While the number might be justified by the platform's scope, it feels heavy, and the server name 'Foodish' suggests a narrower food-focused purpose, creating a mismatch.
As a general data platform, the tool set is comprehensive, covering queries, research, entity resolution, memory, subscriptions, and various analytical tools. Minor gaps exist (e.g., no direct editing or upload capabilities), but the core workflows are well-supported. However, the server name 'Foodish' implies food-related tools, which are minimal, so completeness relative to the name is poor.