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push_prompt

Explains how to create prompts using LangChain templates and push them to LangSmith for version control and management.

Instructions

Call this tool when you need to understand how to create and push prompts to LangSmith.

This is a documentation-only tool that explains how to:

  • Create prompts using LangChain's prompt templates

  • Push prompts to LangSmith for version control and management

  • Handle prompt creation vs. version updates

Use the LangSmith Client's push_prompt() method. See LangSmith documentation for details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosure. It transparently states that it is documentation-only, which is a critical behavioral trait since the name 'push_prompt' could imply an actual push. It also lists the topics it explains. It does not contradict any annotations because none exist, and it communicates the safe, non-mutating nature of the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise, using a short introductory sentence, a clear statement about being documentation-only, and a bulleted list of topics. The final instruction to see LangSmith documentation is useful context. It is well-structured and front-loaded with the most important information, though the last sentence is slightly redundant.

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 zero-parameter, no-output-schema documentation tool, the description is complete. It fully explains the tool's purpose, when to use it, and what specific topics it covers, even pointing to external documentation for further details. There is no ambiguity about what the agent will get from invoking this 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?

The tool has zero parameters, so the schema is trivially complete. The description adds value by outlining what content the documentation covers, which is more useful than parameter explanations. Since the baseline for zero parameters is 4, and the description enhances understanding of the tool's functionality, a 4 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 is a documentation-only resource for understanding how to create and push prompts to LangSmith. It uses specific verbs like 'understand' and 'explains,' and distinguishes itself from sibling tools that actually perform operations like listing or fetching prompts.

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 says 'Call this tool when you need to understand how to create and push prompts,' providing a clear usage condition. It also outlines the topics covered, which helps the agent decide if this tool matches its need. However, it does not explicitly mention when NOT to use it or name alternative sibling tools, but the context is clear for a documentation tool.

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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