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Hnb Hr

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

TDQS

A5/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: fans out to multiple sources in parallel, fallback logic, USPTO soft-fail, return structure (changes[] grouped by source + total_changes + citation URIs). No contradictions with 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?

Single dense paragraph front-loaded with query examples, followed by source details, parameter explanation, return structure, and sibling differentiation. Every sentence adds value, no wasted words.

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?

Highly complete given no output schema: covers all sources, fallback, return format, parameter formats, and when to use alternatives. No missing crucial information.

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?

Schema coverage is 100%, but description adds valuable meaning: examples for 'since' (ISO date and relative shorthand with recommendations), explanation of 'value' (ticker or CIK), and clarification that 'type' only supports 'company'. Exceeds baseline expectation.

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's purpose with specific verbs and resources: 'change feed for a company', fans out to SEC EDGAR, GDELT→GNews, USPTO. It provides query examples ('What's new with X', 'latest on Y') and distinguishes from sibling entity_profile.

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

Usage Guidelines5/5

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

Explicit usage guidance: 'Use entity_profile instead when you want the static profile... regardless of window.' Explains fallback behavior (GDELT→GNews) and parameter recommendations ('Use "30d" or "1m" for typical monitoring').

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

All Hnb Hr toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (beta is currently identical to ask_pipeworx), and deep_research and validate_claim also overlap with the ask_pipeworx family. The Polymarket tools are more distinct, but the query family creates real ambiguity.

Naming Consistency2/5

Tool names mix leading verbs (ask_, compare_, resolve_, scan_) with leading nouns (entity_profile, recent_changes, hnb_currency_rate) and there are multiple subfamily prefixes (ask_pipeworx_*, polymarket_*, hnb_*). All are snake_case, but no consistent verb_noun or noun pattern is followed.

Tool Count2/5

33 tools is well above the 25+ threshold, and the server name ('Hnb Hr') implies a narrow Croatian banking scope while the majority of tools are for a broad data research platform. Many meta-tools (discover_tools, suggest_questions, pipeworx_feedback, pipeworx_trending) inflate the count beyond what the apparent purpose needs.

Completeness3/5

The HNB currency functionality is complete (single and all rates, historical and latest), and the broader data platform offers good coverage via the ask_pipeworx router, subscriptions, and memory tools. However, there are gaps (no direct HNB news or historical archive tool, patents soft-fail, and out-of-domain tools like generate_llms_txt), and the mismatch between server name and content leaves the surface feeling incomplete.

Resources