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Could Have Been Email

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds beyond these: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This provides transparency on data sources, privacy, and caching behavior, which is highly valuable.

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 with two well-structured paragraphs. The first sentence front-loads the core purpose. Every sentence adds value, with no redundancy or filler. Use cases are enumerated clearly, and technical details are grouped separately.

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 the low complexity (one optional parameter, no output schema), the description covers all necessary aspects: return values (top tools, packs, volume), window options, use cases, data source, privacy, and caching. There are no missing details for an agent to correctly invoke this tool.

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 a description and enum for the single parameter. The description adds context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enhances the semantic understanding beyond the raw schema, justifying a score above the baseline of 3.

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' with specific time windows. It uses a specific verb ('returns') and resource ('trending data on Pipeworx'), and distinguishes from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on aggregate trends.

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?

Three explicit use cases are provided (e.g., discovering hot data sources, confirming canonical tools). The description also explains when to use different window sizes: 'shorter windows surface what's hot right now; longer windows show steady-state demand.' However, it does not explicitly compare against sibling tools or state when not to use it.

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

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TDQS

A3.9/5.0
Disambiguation2/5

Several tool clusters are nearly indistinguishable in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and the six polymarket tools heavily overlap in surfacing prediction-market edge. Even with detailed descriptions, an agent could easily misselect between bet_research and polymarket_edges or between discover_tools and suggest_questions.

Naming Consistency3/5

Most names use lowercase snake_case, but the pattern is mixed: some are verb_noun (compare_entities, resolve_entity), some are bare verbs (remember, forget, recall), and some are compound noun phrases (polymarket_edges, pipeworx_trending). ask_pipeworx also breaks the separator convention compared to ask_pipeworx_beta and ask_pipeworx_grounded.

Tool Count2/5

With 32 tools, this exceeds the 25+ threshold for 'too many' and feels like a platform bundle rather than a focused server. It spans data querying, prediction markets, memory, subscriptions, feedback, AI visibility, dependency scanning, and llms.txt generation, which is far more surface area than one coherent server should present.

Completeness4/5

For the core data-research and prediction-market domains, coverage is strong: query, grounded verification, deep research, entity resolution, comparisons, change feeds, arbitrage, fill-risk, subscriptions, and memory are all present with no major dead ends. The gaps are mostly the single-purpose oddballs (could_have_been_email_analyze, generate_llms_txt, scan_dependency) that don't connect to the rest of the surface.

Resources