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Bnr Ro

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

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral details: embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character cap (200K chars with truncation flag), and that passages include offsets for verification. 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?

The description is concise (four sentences) and well-structured. The first sentence states the core purpose. Subsequent sentences add precision, use cases, and technical details. Every sentence earns its place with no redundancy 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 no output schema, the description adequately explains return values (passages with offsets and scores). It covers all necessary context: when to use, how it works (embeddings, windows, cap), pairing with sibling tool, and limitations (200K char cap). The description is complete for an effective tool.

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% with three parameters. The description adds meaning: for 'text' it specifies max length and purpose; for 'query' it provides natural-language examples; for 'limit' it gives range (1-20) and default (5). This context helps the agent select appropriate values beyond schema descriptions.

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 function: semantic search inside a fetched record. It specifies the inputs (text and natural-language query) and outputs (top-N passages with offsets and scores), and distinguishes it from siblings by mentioning 'ask_pipeworx_grounded' and explaining how they pair.

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?

The description explicitly says when to use this tool: 'when the record is too big to cram into the prompt.' It also provides guidance on pairing with 'ask_pipeworx_grounded,' and contrasts with alternatives by stating it saves context and returns only relevant passages.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim, which could cause confusion. However, the descriptions help clarify when to use each.

Naming Consistency3/5

Tool names use a mix of patterns (verb_noun, noun_noun, adjective_noun) but are consistently lowercase with underscores. Some names are vague like 'forever' and 'recent_alerts', but overall readable.

Tool Count3/5

32 tools is on the high side for a single server, but many are specialized and serve a broad data query platform. Some tools are meta-tools covering multiple use cases, which could reduce the need for so many.

Completeness4/5

The tool set covers a wide range of functionalities including data querying, entity resolution, comparisons, verification, memory, subscriptions, and prediction markets. Minor gaps like data export are not critical for its purpose.

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