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RTFD (Read The F*****g Docs)

by aserper

fetch_dockerfile

Retrieve Dockerfile content to analyze image composition, identify dependencies, and understand build processes for security auditing or learning purposes.

Instructions

        Fetch the actual Dockerfile used to build a Docker image.

        USE THIS WHEN: You need to see exactly how an image is built (base image, installed packages, configuration).

        BEST FOR: Understanding image composition, security analysis, or learning how to build similar images.
        Attempts to find Dockerfile link in DockerHub description and fetches from source (usually GitHub).

        Useful for:
        - Seeing what base image is used
        - Identifying installed packages and dependencies
        - Understanding build process and optimizations
        - Security auditing (what's included in the image)
        - Learning Dockerfile best practices from official images

        Note: Not all images have publicly accessible Dockerfiles. Many official images do.

        Args:
            image: Docker image name (e.g., "nginx", "python", "postgres")

        Returns:
            JSON with Dockerfile content, source URL, and metadata (or error if not found)

        Example: fetch_dockerfile("nginx") → Returns Dockerfile from nginx GitHub repository
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it describes the method ('Attempts to find Dockerfile link in DockerHub description and fetches from source'), limitations ('Not all images have publicly accessible Dockerfiles'), and typical outcomes ('Many official images do'). It could improve by mentioning rate limits or authentication needs, but covers essential operational context.

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?

Well-structured with front-loaded purpose, usage guidelines, and key details, followed by bullet points for clarity. Every sentence adds value without redundancy, and the example at the end reinforces understanding efficiently.

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 no annotations, 0% schema coverage, and no output schema, the description provides comprehensive context: it explains the tool's purpose, usage, behavior, parameters, returns (JSON with content, URL, metadata, or error), and includes an example. This fully compensates for the lack of structured data.

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 description coverage is 0%, so the description must compensate fully. It clearly explains the single parameter 'image' with examples ('e.g., "nginx", "python", "postgres"') and context on what it represents ('Docker image name'), adding significant meaning beyond the bare 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 specific action ('fetch the actual Dockerfile'), resource ('used to build a Docker image'), and distinguishes it from sibling tools like docker_image_metadata or search_docker_images by focusing on retrieving the build file itself rather than metadata or search results.

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?

Explicitly provides 'USE THIS WHEN' and 'BEST FOR' sections that detail when to use this tool (e.g., for seeing how an image is built, security analysis) and when it might not work ('Not all images have publicly accessible Dockerfiles'), with clear alternatives implied by sibling tools like docker_image_metadata for different needs.

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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