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Market Recap

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?

Goes beyond annotations by disclosing the embedding model (BGE-base-en), cosine similarity, 500-char overlapping windows, return contents (passages with offsets and scores), and the 200K char cap with truncation flag. No contradiction with readOnlyHint.

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?

Four sentences with clear front-loading of purpose. Each sentence earns its place, covering purpose, use case, sibling relation, and technical details 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?

For a read-only semantic search tool, the description fully covers what it does, when to use it, how it works, and key constraints. Annotations cover safety, so no gaps remain.

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 already describes all parameters, but the description adds extra context: real-world examples for text (SEC 10-K, article), query examples, and 'top-N' framing for limit. This 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?

First sentence states 'Semantic search INSIDE a fetched record' with a specific verb and resource. It distinguishes from siblings by emphasizing searching within already-fetched text and explicitly pairs with ask_pipeworx_grounded.

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?

Provides explicit when-to-use: 'Use when the record is too big to cram into the prompt.' It also names an alternative approach (ask_pipeworx_grounded) and explains how they complement each other.

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

A3.8/5.0
Disambiguation2/5

Many tools overlap in purpose (e.g., multiple Polymarket analysis tools, multiple AI visibility tools, ask_pipeworx vs deep_research). Agents will have difficulty choosing the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names use a mix of styles (snake_case, descriptive phrases) without a consistent verb_noun pattern. For example, 'ask_pipeworx' and 'bet_research' have different naming conventions. This inconsistency makes it harder for agents to predict tool names.

Tool Count2/5

32 tools is on the high side for a single server. Many tools could be merged (e.g., multiple polymarket tools). The count feels excessive for the scope, causing cognitive load and potential selection errors.

Completeness3/5

The tool set covers a wide range of domains (prediction markets, company data, fact-checking, etc.) but has notable gaps (e.g., limited entity types for company/drug only). Redundancy in some areas makes the set feel bloated rather than complete.

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