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Open Sanctions

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds rich behavioral details: returns top-N passages with offsets and similarity scores, 200K char cap with truncation flag, BGE-base-en embeddings, 500-char windows, cosine similarity.

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?

Single paragraph, front-loaded with purpose and use case. Every sentence adds value (usage, pairing, technical details, cap). Slightly dense but efficient; no 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, description covers return format (passages with offsets and scores) and technical implementation. For a 3-param, read-only tool, this is comprehensive and leaves no gaps.

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% with descriptions for all 3 parameters. Description adds value by providing examples for query, stating default for limit, and reiterating the 200K char cap for text. Enhances usability 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?

The description clearly states it performs semantic search inside a fetched record, with specific examples (SEC 10-K body, article). It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and implying it's for documents too large for prompts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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 mentions pairing with ask_pipeworx_grounded. However, it does not explicitly state when not to use it, though context implies 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
Disambiguation2/5

Multiple tools have overlapping purposes, especially the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research) which all serve to query data but with subtle differences. An agent would struggle to distinguish which to use without deep understanding of their nuances.

Naming Consistency3/5

Most tool names follow a verb_noun or noun_noun pattern in snake_case, but there are exceptions like 'forget', 'recall', 'remember' which are single verbs, and names like 'ask_pipeworx' mix verb and proper noun. Overall pattern is discernible but not uniform.

Tool Count2/5

33 tools is high for a server named 'Open Sanctions' which implies a focused domain. The tool set spans sanctions, meta-querying, prediction markets, memory, subscriptions, and more, making it feel bloated and unfocused relative to the server's name.

Completeness2/5

For a sanctions server, only two tools (search_entities, get_entity) directly address the domain, missing obvious operations like update, delete, or list. Many tools are unrelated to sanctions, leaving the core domain incomplete.

Resources