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Hnb Hr

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds details about the embedding model (BGE-base-en), windowing (500-char overlapping), character limit (200K chars with truncation flag), and that results include offsets for verification. No contradictions.

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 (about 6 lines) and well-structured. Each sentence serves a purpose: stating the action, usage context, technical details, and limitations. No redundant information.

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 tool's complexity (3 parameters, no output schema), the description covers all necessary aspects: purpose, usage, behavioral details, parameter semantics, and return format (passages with offsets and scores). It is complete 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 coverage is 100% with descriptions for all parameters. The description adds value beyond schema by providing example queries for the 'query' parameter, clarifying the max characters for 'text', and explaining the cap behavior. However, it could further detail the 'limit' parameter's default and range, which is already implied.

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 performs semantic search inside a fetched record, specifying the verb (search) and resource (inside a record). It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and emphasizing context-saving for large records.

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?

Explicitly states when to use: 'when the record is too big to cram into the prompt' and provides an alternative strategy: pairing with ask_pipeworx_grounded. This gives clear guidance on tool selection.

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