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Whiskyhunter

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 declare readOnly, openWorld, idempotent, and non-destructive. Description adds specific technical behavior: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag. No 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?

Single dense paragraph front-loads purpose, then usage, then technical details. Every sentence adds value without redundancy.

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?

No output schema, but description explains return format (top-N passages with offsets and similarity scores) and truncation behavior. Fully sufficient for agent to understand tool's capabilities.

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%, but description enhances understanding with practical examples for query and explicit max char limit for text. Parameters are well-described beyond basic schema.

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?

Clearly states it performs semantic search inside a fetched record, with concrete examples (SEC 10-K, article, tool result). Distinguishes from siblings like ask_pipeworx_grounded, and specifies it saves context.

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 says when to use (record too big for prompt) and pairs with ask_pipeworx_grounded for grounded Q&A. Provides clear guidelines for agent decision-making.

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

All Whiskyhunter toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research all perform research; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker are similar). Agents may struggle to pick the correct tool.

Naming Consistency2/5

Naming is inconsistent: some use snake_case (list_distilleries), others are descriptive (ask_pipeworx, deep_research), and some are branded (pipeworx_feedback). No clear pattern.

Tool Count3/5

33 tools is high but not unreasonable given the broad scope. However, the server name implies a whisky focus, making the count feel excessive for that domain.

Completeness2/5

For the whisky domain suggested by the name, only 3 tools are relevant. The rest are generic or unrelated (Polymarket, npm scanning), leaving significant gaps for whisky-specific tasks.

Resources