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Data Brussels

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 embeddings model (BGE-base-en), window size (500-char overlapping), character cap (200K with truncation and flag), and return type (passages with offsets and scores). Adds significant detail beyond 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?

Single paragraph with front-loaded purpose, followed by usage guidance and technical details. Every sentence is informative with no 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?

Covers purpose, usage, technical details, return format, and integration with other tools. Adequate for a 3-parameter tool without output schema.

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 covers 100% of parameters. Description adds real-world examples (e.g., SEC filing text, specific queries) and clarifies constraints (max chars, default limit). Adds meaning beyond 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 'Semantic search INSIDE a fetched record' with a specific verb and resource. Distinguishes from siblings by noting it pairs with ask_pipeworx_grounded and is for when records are too large for the prompt.

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 'Use when the record is too big to cram into the prompt' and suggests pairing with ask_pipeworx_grounded. Provides clear context for when to use versus 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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, with clear descriptions differentiating similar ones like ask_pipeworx and ask_pipeworx_grounded. However, some overlap exists between deep_research and ask_pipeworx, though descriptions provide guidance.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ai_visibility_check), multi-word phrases (scan_competitor_ai_presence), and simple verbs (query, recall). No uniform pattern like verb_noun convention.

Tool Count3/5

33 tools is on the high side, but the server covers a broad domain (data querying, prediction markets, subscriptions). It feels slightly heavy but still manageable; borderline between reasonable and excessive.

Completeness3/5

The Pipeworx and prediction market tools are comprehensive, but Brussels Open Data is underrepresented with only three tools (query, dataset_info, search_datasets). Missing update/delete operations for Brussels data, though likely read-only.

Resources