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Color Pizza

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

A5/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds substantial detail beyond annotations: uses BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, cap of 200K characters with truncation flagged. This fully informs the agent of underlying mechanics and constraints.

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?

Three sentences: first states purpose, second explains when to use, third provides technical specifics. No filler. Every sentence is essential and front-loaded. The structure is optimal for quick agent understanding.

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?

Despite no output schema, the description covers return structure (top-N passages with offsets and similarity scores), input limits, and truncation behavior. It also notes the algorithm and windowing, giving the agent all necessary context to invoke and interpret results. No gaps identified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already covers all parameters with descriptions (100% coverage). The description adds practical examples for 'query' (e.g. 'supply-chain risk'), clarifies the 'text' limit as 'max ~200K chars', and explains that each returned passage carries an offset for quote verification. This enriches the agent's understanding of how to use parameters effectively.

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 explicitly states 'Semantic search INSIDE a fetched record' and lists what it returns (top-N passages with offsets and scores). The title 'Search Within a Source' matches the purpose. It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded, implying it is for document-level search rather than broader search tools.

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?

The description clearly says 'Use when the record is too big to cram into the prompt' and explains how it saves context. It also provides an alternative workflow: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This gives explicit when-to-use and alternative tool guidance.

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 distinct purposes, such as `entity_profile` for company profiles and `recent_changes` for updates. However, some overlap exists between `ask_pipeworx` and `ask_pipeworx_grounded` (both query data, one with hallucination resistance), and among Polymarket-related tools (`bet_research`, `polymarket_edges`, `polymarket_arbitrage`). Detailed descriptions help distinguish them.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., `ai_visibility_check`, `ask_pipeworx`, `bet_research`, `compare_entities`). No mixing of camelCase or other conventions. The names are descriptive and predictable.

Tool Count3/5

At 29 tools, the server is on the heavy side. While each tool serves a specific function, the broad scope (color naming, Polymarket betting, company research, etc.) makes the count feel slightly high for a single server, though still manageable.

Completeness4/5

The tool set covers color naming, company research, Polymarket, AI visibility, memory, subscriptions, and data queries comprehensively. Minor gaps exist (e.g., no direct SEC filing retrieval without compound tools), but overall it provides a well-rounded surface for its diverse domain.

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