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Kolada Se

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?

Description discloses caching behavior, data source (CF analytics-engine), no PII, and self-aggregating nature. Annotations already mark readOnly, idempotent, and nondestructive; the description adds valuable operational context without contradiction.

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?

Extremely concise: three sentences plus a bullet list. Front-loaded with output summary, no redundant phrasing. Every sentence earns its place.

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 tool with one optional parameter, no output schema, and strong annotations, the description fully covers purpose, use cases, behavior, and parameter. No gaps remain.

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% for the single parameter, so baseline is 3. Description adds value by explaining the meaning of different window choices ('hot right now' vs 'steady-state demand'), exceeding schema documentation.

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 differentiates from siblings like discover_tools by focusing on trending/aggregate data rather than search or entity discovery.

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?

Explicitly lists three specific use cases, providing actionable context for when to use. Could be stronger by stating when not to use or naming alternatives, but the guidance is clear and practical.

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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly identical when no routing candidate is active), ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions through the same underlying router. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) has substantial purpose overlap that requires reading long descriptions to disambiguate.

Naming Consistency3/5

Mostly snake_case, and the pipeworx_/polymarket_/ask_ prefixes give some structure, but conventions are mixed: some tools are verb_noun (list_municipalities, get_data), some are bare verbs (forget, recall, remember), and some are noun phrases (entity_profile, deep_research, bet_research, recent_changes). The inconsistent prefixing across meta-tools (ask_, deep_, entity_, scan_, validate_) makes the surface feel less predictable than it could be.

Tool Count2/5

35 tools is on the heavy side, but the bigger problem is that the server name 'Kolada Se' matches only 4 tools (search_kpi, list_municipalities, list_org_units, get_data), while the other 31 tools belong to unrelated domains (Pipeworx data routing, prediction markets, memory, subscriptions, AI visibility). This is a severe scope mismatch that makes the count feel bloated and unfocused.

Completeness3/5

For the Kolada domain named by the server, the surface is minimal: you can search KPIs, list municipalities, list org units, and fetch single-KPI data, but there is no multi-year bulk fetch, cross-municipality comparison, or unit-level data retrieval. The broader Pipeworx/prediction-market surface is fairly feature-complete, but it is not what the server name implies, so the set as a whole leaves the apparent domain thinly covered.

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