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Ship On Friday

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

A4.4/5.0
Behavior5/5

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

Annotations already indicate safe, read-only, and idempotent behavior. The description adds rich detail: BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char limit with truncation flag, and output includes offsets and scores. No contradictions 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?

Single paragraph of 4-5 sentences, front-loaded with core purpose. Every sentence contributes meaningful information without 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?

No output schema, but description fully explains return values (top-N passages with offsets and scores). Also covers embedding details, windowing, and input limits. Comprehensive for a 3-parameter tool.

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 provides 100% coverage of parameters with descriptions. The description adds context: 'Pass the text you already pulled' for text, example queries, and hints at limit range. This extra context justifies a score above baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs semantic search inside a fetched record, saving context by returning only relevant passages. It mentions pairing with ask_pipeworx_grounded but does not explicitly differentiate from sibling search tools like ask_pipeworx or deep_research, which slightly reduces clarity.

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 advises using when the record is too large for the prompt, and suggests pairing with ask_pipeworx_grounded. However, it does not provide when-not-to-use guidance or compare with alternative tools.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among data query tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, and recent_changes. However, detailed descriptions and different use cases help an agent distinguish them, so it is mostly clear.

Naming Consistency5/5

All tool names follow a consistent lower_snake_case pattern with a verb_noun style (e.g., ask_pipeworx, list_subscriptions, validate_claim). There are no mixed conventions, making it predictable and easy to understand.

Tool Count4/5

With 31 tools, the server covers a broad domain of data queries, prediction markets, memory, and subscriptions. While slightly more than typical, each tool earns its place and the count is reasonable for the comprehensive platform scope.

Completeness5/5

The tool surface is extensive, covering data querying, analysis, entity resolution, comparison, change tracking, memory, subscriptions, and more. There are no obvious gaps; it supports a wide range of user intents for the server's purpose.

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