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timps_rag_designer

Design RAG pipelines for LlamaIndex or LangChain, covering chunking, embedding, retrieval, and reranking. Specify your task in plain English to get tailored code and architecture in your preferred language.

Instructions

Design chunking/embedding/retrieval/reranking RAG pipelines for LlamaIndex or LangChain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestNoPlain-English task or context for the agent.
languageNoPrimary programming language (default: python).python
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states that the tool 'designs' pipelines, but does not specify what the output or action will be (e.g., does it generate code, produce a plan, return a sequence of steps?). It lacks details on side effects, required inputs, or whether it modifies any files. This is a significant gap for a tool with no annotation support.

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 a single, tightly crafted sentence that front-loads the core purpose. It avoids redundancy and conveys the essential information without fluff. Every word contributes to the meaning, making it an excellent model of conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of RAG pipeline design, the description is too sparse. It does not clarify what the 'design' entails—whether it returns a step-by-step plan, code snippets, a configuration, or something else. The absence of an output schema increases the need for the description to set expectations, which it fails to do. Sibling tools like timps_rag_evaluator have more specific roles, but this one leaves users guessing about its deliverable.

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

Parameters3/5

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

The input schema already provides descriptive semantics for both parameters: 'request' (plain-English task) and 'language' (programming language). With 100% schema coverage, the description does not need to add parameter details. The description adds no extra meaning beyond the schema, so the baseline of 3 is appropriate.

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 purpose: designing RAG pipelines with specific components (chunking, embedding, retrieval, reranking) and target frameworks (LlamaIndex or LangChain). It uses a specific verb ('design') and resource ('RAG pipelines'), distinguishing it from siblings like timps_rag_evaluator or timps_local_rag_builder which focus on evaluation or building.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for the design phase of RAG pipelines, but does not explicitly state when to use it versus alternatives. It does not provide exclusions or mention related tools (e.g., 'use timps_rag_evaluator for evaluation'). The 'design' verb suggests it is not for implementation, but this is implicit rather than explicit.

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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