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Dryad

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

A4.7/5.0
Behavior5/5

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

The description discloses important behaviors: uses BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char cap with truncation flagging, and returns character offsets. This goes beyond the annotations which only indicate read-only, idempotent, and non-destructive.

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 concise yet comprehensive, covering purpose, usage, technical details, and pairing with another tool in a single paragraph. No unnecessary words; information is front-loaded.

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 the tool's complexity (3 parameters, no output schema), the description fully explains what the tool returns (passages with offsets and scores), its limitations (200K char cap, truncation), and model details. The agent has all necessary information to use the tool correctly.

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 description coverage is 100%, so the schema already documents all three parameters. The description adds value by providing usage context (e.g., examples for the query parameter) and reinforcing the length cap for text. It enhances understanding beyond the 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 the tool's function: semantic search inside a fetched record. It distinguishes from sibling tools like ask_pipeworx_grounded by explaining the pairing and use case.

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?

The description explicitly tells when to use the tool: when the record is too large for the prompt. It provides context on saving prompt space and returning only relevant passages. While it doesn't list when-not-to-use, the alternative tool is mentioned.

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

A4.1/5.0
Disambiguation3/5

There is notable overlap between tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, as well as between polymarket_edges and bet_research. While descriptions differentiate them, an agent may struggle to pick the right one without deep understanding of nuances.

Naming Consistency3/5

Tool names use mixed conventions: snake_case (ai_visibility_check), verb_noun (ask_pipeworx, bet_research), and single-word verbs (forget, recall). Some names are very long (polymarket_fill_risk) while others are terse, reducing predictability.

Tool Count3/5

33 tools is high but can be justified by the broad domain coverage (data retrieval, entity resolution, comparisons, memory, subscriptions). However, several tools serve similar purposes, suggesting potential consolidation.

Completeness4/5

The tool set covers a wide range of data sources and tasks (SEC, FDA, real estate, prediction markets, etc.) with CRUD-like operations on memory and subscriptions. Minor gaps exist, such as no direct stock trading or social media monitoring, but overall coverage is strong.

Resources