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Data Edmonton

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 readOnly/openWorld/idempotent, and the description adds valuable behavioral context: results are cached 5min-1h, derived from CF analytics-engine, no PII, and returns only (pack, tool, count). It also clarifies the 'right now' caveat due to caching, exceeding annotation coverage.

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 front-loaded with the primary function, then purposeful use cases and technical details. Every sentence adds distinct information (what, why, caching, privacy, data shape) without repetition; the structured 'Useful for' list makes scanning easy.

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?

For a simple read-only tool with one optional parameter and no output schema, the description fully covers return contents, privacy, aggregation source, caching behavior, and window interpretation. It is self-sufficient for an agent to select and invoke correctly.

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 covers the single window parameter with enum and description, so baseline is 3. The description adds value by explaining trade-offs ('Shorter windows surface what's hot right now; longer windows show steady-state demand'), which helps choose the right value.

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?

Description opens with a clear, specific verb and object: 'What other AI agents are calling on Pipeworx right now' and 'Returns the top tools, top packs, and total call volume.' It distinguishes itself from siblings like discover_tools by emphasizing aggregate popularity signal and explicitly scoping to recent windows (24h/7d/30d).

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?

Provides explicit use cases: discovering hot data sources, confirming canonical tool choice, and checking alignment with agent demand. Also explains window semantics (short = hot right now, long = steady-state), but doesn't name alternative tools or exclusions, so slightly below a 5.

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

A3.8/5.0
Disambiguation2/5

Several tool clusters have significantly overlapping scopes. ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx_grounded, deep_research, and validate_claim all return grounded answers with different levels of verification. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also has fuzzy boundaries that could cause misselection.

Naming Consistency4/5

The vast majority of tools follow a verb_noun snake_case pattern (ask_pipeworx, compare_entities, scan_dependency). A few nouns like entity_profile and recent_alerts deviate slightly, and the memory trio (remember, recall, forget) are single-word verbs, but the overall style is consistent and readable.

Tool Count2/5

34 tools is excessive for a server whose name suggests a focused Edmonton open-data scope; only 3 tools actually relate to Edmonton data. Even as a general data platform, the count exceeds the 25-tool threshold and includes many meta-tools (discover_tools, suggest_questions, pipeworx_feedback) that could be consolidated.

Completeness4/5

For the Edmonton open-data subset, search, query, and recent-records cover the core lifecycle well. The broader Pipeworx toolset is comprehensive (entity profiles, comparisons, claims, subscriptions, memory), with only minor gaps like a direct catalog-browsing tool for the 1393 sources.

Resources