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Glama

Gets Nz

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds caching behavior (5min-1h), data source (CF analytics-engine), privacy (no PII), and self-aggregating nature, going well beyond annotations.

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: one sentence for output, one sentence for use cases, one for technical details. Every sentence adds value with no repetition or fluff.

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 simple read-only nature, one optional parameter, and no output schema, the description covers outputs, caching, privacy, and parameter effects completely.

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

Parameters5/5

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

The input schema has one parameter with enum and description. The description adds meaning by explaining that shorter windows surface hot items and longer windows show steady-state demand, complementing the schema fully.

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, packs, and call volume over a window. The verb 'returns' and explicit outputs distinguish it from siblings like 'ask_pipeworx' or 'discover_tools'.

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 provides three explicit use cases (discovering hot data sources, confirming canonical tools, aligning use cases). While it doesn't explicitly say when not to use, the context is clear and differentiated from sibling tools.

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

Multiple tools overlap significantly: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly identical purposes, and deep_research further duplicates. Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) also have overlapping scopes, making it hard for an agent to select the correct one without deep inspection.

Naming Consistency3/5

Tool names use a mix of patterns: some are descriptive phrases (ai_visibility_check, generate_llms_txt), others are domain-prefixed (nz_tender_*, polymarket_*) but lack a uniform verb_noun structure. The ask_pipeworx series diverges from the rest, and verb choices are inconsistent (compare_entities vs scan_competitor_ai_presence).

Tool Count2/5

With 34 tools, the surface is large and feels bloated. The server covers multiple distinct domains (NZ tenders, Polymarket, memory, subscriptions) that could be separate servers. Many tools are variants of the same core functionality (e.g., four ask_pipeworx variants), inflating the count without clear necessity.

Completeness3/5

For a general-purpose data query server, the tool set covers a broad range of sources (SEC, FDA, FRED, etc.) and includes CRUD for memory and subscriptions. However, obvious gaps exist: no dedicated web search tool (ask_pipeworx is for structured data), and NZ coverage is limited to tenders only. Missing update/delete for some resources (e.g., no way to modify a subscription beyond unsubscribe).

Resources