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Vizier

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

A4.8/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations (readOnlyHint, idempotentHint, etc.) by disclosing the embedding model (BGE-base-en), windowing (overlapping 500-char windows), character cap (200K chars with truncation flagging), and return format (offsets, similarity scores). No contradiction 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?

The description is two substantive sentences plus a concise technical note. Every sentence is essential, front-loaded with the core action, and there is no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description adequately explains input details, behavior (embeddings, windowing, truncation), and return fields (passages with offsets and scores). It does not detail the exact JSON response structure, but the context is sufficient for selection and invocation.

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%, and the description adds value by specifying the limit's range (1-20) and default (5), and by providing natural-language query examples. The schema already describes each parameter well, but the description enhances clarity.

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 'semantic search inside a fetched record' and provides concrete examples (SEC 10-K, article, tool result). It contrasts with siblings by mentioning it pairs with ask_pipeworx_grounded, distinguishing its purpose effectively.

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 advises when to use this tool: 'Use when the record is too big to cram into the prompt' and explains how it saves context and returns relevant passages. It also suggests pairing with ask_pipeworx_grounded, providing clear guidance on alternatives.

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

A3.6/5.0
Disambiguation3/5

Several tools occupy adjacent roles: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, discover_tools and suggest_questions both serve discovery, and the astronomy plus Polymarket scanners have overlapping boundaries. The descriptions are unusually detailed and do differentiate most tools, but the number of near-neighbor tools still creates real selection risk.

Naming Consistency3/5

Names are uniformly snake_case and mostly descriptive, which helps, but the grammatical pattern is inconsistent: verb_noun names (compare_entities, resolve_entity) sit alongside bare nouns (catalogs, object) and bare verbs (remember, recall, forget). It is readable but not a predictable verb_noun convention.

Tool Count2/5

At 35 tools, the surface is well beyond what an agent can comfortably hold in mind. The set mixes a data-research core with one-off utilities like generate_llms_txt, scan_dependency, and AI-visibility auditing, making it feel like a grab-bag rather than a scoped server.

Completeness4/5

The core data-research workflow is strongly covered: plain and grounded Q&A, deep research, entity resolution, profiles, comparisons, claim validation, recent changes, tool discovery, memory, and subscription lifecycle all exist. Minor gaps remain, such as no subscription-update operation and no generic citation-fetch tool, but there are no serious dead ends.

Resources