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Data Ua

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?

Discloses technical details beyond annotations: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, character offsets, and a 200K char cap with truncation flagging. Annotations already indicate read-only, idempotent, and non-destructive, which the description complements without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly efficient but includes minor technical details (e.g., embedding model, window size) that could be considered extra. However, it is well-structured with a clear opening and logical flow. Slightly longer than necessary but still effective.

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?

Despite no output schema, the description thoroughly explains what the tool returns (top-N passages with character offsets and similarity scores). It also provides integration advice (pairing with ask_pipeworx_grounded) and behavior under input limits (truncation). Complete for a search 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% (all parameters have descriptions). The description adds value by providing concrete examples for 'query' and specifying the max length for 'text' (200K chars), which is not in the schema description. Limit default and range are also clarified.

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 defines the tool as 'Semantic search INSIDE a fetched record.' It specifies the action (search), the object ('record'), and the scope ('inside'). It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and saving 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 states when to use: 'when the record is too big to cram into the prompt.' Also describes what it returns and how it helps verify verbatim quotes. Mentions an alternative strategy (ask_pipeworx_grounded) for grounding.

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

All Data Ua toolsInspect
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TDQS

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, though the three ask_pipeworx variants (standard, beta, grounded) are very similar, potentially causing confusion. The Polymarket and memory tool families are well-differentiated.

Naming Consistency5/5

All tool names use snake_case consistently, with a clear verb_noun pattern (e.g., resolve_entity, search_datasets, subscribe). No mixing of conventions.

Tool Count4/5

34 tools is high but justified given the breadth of the Pipeworx platform and Ukraine Open Data integration. The set covers a wide range of data sources and operations without feeling bloated.

Completeness4/5

The tool surface is thorough, covering querying, comparison, profiling, subscriptions, and memory. Minor redundancy in ask_pipeworx variants, but no significant gaps for the stated data-access purpose.

Resources