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Hadar301

openshift-installer-checker

by Hadar301
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Python

fetch_repo_content

Analyze a Git repository to extract and list application requirements from README and deployment YAML files. Get hardware and software needs without any cluster involvement.

Instructions

Extract and list application requirements from a repository. NO cluster involvement.

⚠️ ALWAYS USE THIS TOOL when user asks about requirements WITHOUT mentioning cluster:
- "what are the requirements for X?"
- "what does X need to run?"
- "analyze requirements for X"
- "list the requirements for X"
- "what hardware/software does X need?"

KEY: If the question is ONLY about the app's needs (not about "can I install it?"), use this tool.

⛔ DO NOT USE when user asks about deployment/installation/compatibility with their cluster.
For those questions, use check_feasibility instead.

This tool ONLY analyzes the repository. It does NOT:
- Scan any cluster
- Check cluster compatibility
- Determine if installation is possible

Args:
    repo_url: Full GitHub or GitLab repository URL
              Examples:
              - https://github.com/nvidia/nemo
              - https://gitlab.com/project/repo

Returns:
    Dictionary containing:
    - success: Boolean indicating if the operation was successful
    - repo_info: Repository metadata (platform, owner, repo name)
    - readme_content: README text for LLM to analyze
    - deployment_files: List of deployment YAML files with content and parsed resources
    - yaml_extracted_requirements: Summary of requirements found in YAML files
    - instructions_for_llm: Guidance for the LLM on how to analyze the data

Example:
    >>> fetch_repo_content("https://github.com/kubernetes/kubernetes")
    {
        "success": True,
        "repo_info": {...},
        "readme_content": "# Kubernetes...",
        "deployment_files": [...],
        "yaml_extracted_requirements": {...}
    }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_urlYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
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, and it handles this well: it clearly states the tool ONLY analyzes the repository and does NOT scan a cluster, check compatibility, or determine installation feasibility. It does not explicitly mention side effects or network/auth behavior, but the operational exclusions are strong enough to prevent common misuse.

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 longer than minimal, but every section serves a purpose: trigger phrases, exclusions, parameter details, and return structure. Some redundancy exists ('NO cluster involvement' appears several ways), but the formatting with warnings, bullet lists, and an example keeps it scannable.

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?

There is no output schema, so the description correctly enumerates the return dictionary keys and provides a full usage example. Combined with the parameter guidance and explicit behavioral exclusions, the agent has everything needed to invoke the tool correctly and interpret its result.

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?

The schema has zero description coverage for repo_url, but the description fully compensates by specifying 'Full GitHub or GitLab repository URL' and providing concrete examples for both platforms. This gives the agent exactly the format and valid input space needed.

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 opens with a specific verb-resource pair ('Extract and list application requirements from a repository') and immediately disambiguates from siblings by stating 'NO cluster involvement' and pointing to check_feasibility for cluster questions. The tool's scope is unmistakable.

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?

It gives explicit trigger phrases for when the tool should always be used, a KEY decision rule ('not about can I install it?'), and an explicit DO NOT USE condition that routes deployment/installation/compatibility questions to check_feasibility. This is exemplary routing 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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