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Foodish

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral info: self-aggregating signal, no PII, caching behavior (5min-1h). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, well-structured, and front-loaded with the core functionality. Every sentence adds value; no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one optional parameter (with enum) and no output schema, the description fully covers the input semantics and expected output (top tools, packs, volume). No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with enumerated values. The description adds semantic guidance beyond the schema by explaining that shorter windows surface hot trends and longer windows show steady-state demand.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It uses specific verbs ('returns') and resources, and distinguishes itself from siblings like 'discover_tools' by focusing on what agents are calling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists three concrete use cases (discovering hot data sources, confirming canonical tools, aligning use cases). While it doesn't mention when not to use or alternatives, the use cases provide strong guidance.

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

All Foodish toolsInspect
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TDQS

A3.8/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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).

Tool Count3/5

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.

Completeness4/5

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.

Resources