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list_available_frameworks

Discover available reasoning frameworks to understand their purposes and identify the best approach for specific tasks.

Instructions

List all available reasoning frameworks with their descriptions.

Use this to understand what frameworks are available and when each is best used.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The MCP tool `list_available_frameworks` is implemented in `src/promptcore/main.py` using the `@mcp.tool()` decorator. It iterates over `FRAMEWORK_REGISTRY` to return a list of available reasoning frameworks with their details.
    @mcp.tool()
    def list_available_frameworks() -> dict:
        """
        List all available reasoning frameworks with their descriptions.
        
        Use this to understand what frameworks are available and when each is best used.
        """
        # This tool reads from static registry, valid to stay static or move to selector
        # For consistency, we can leave it as is since FRAMEWORK_REGISTRY is a constant
        frameworks = []
        for name, cls in FRAMEWORK_REGISTRY.items():
            frameworks.append({
                "name": name,
                "description": cls.description,
                "best_for": [cat.value for cat in cls.best_for],
                "complexity_threshold": cls.complexity_threshold,
            })
        
        return {
            "frameworks": frameworks,
            "count": len(frameworks),
        }
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the tool as a list operation, implying it's read-only and non-destructive, which is helpful. However, it lacks details on behavioral traits like rate limits, authentication needs, pagination, or response format. The description adds basic context but doesn't fully compensate for the absence of annotations.

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 two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured. There's no wasted text, and it's appropriately sized for the tool's complexity.

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

Completeness4/5

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

Given the tool has 0 parameters, no annotations, and an output schema exists (which handles return values), the description is reasonably complete. It covers the purpose and usage context adequately. However, it could be more comprehensive by addressing potential behavioral aspects like error handling or data freshness, but the output schema reduces the need for extensive detail.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. A baseline of 4 is applied for tools with no parameters, as there's nothing to compensate for, and the description doesn't introduce confusion.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('List') and resource ('all available reasoning frameworks with their descriptions'). It distinguishes itself from siblings like 'generate_meta_prompt' or 'recommend_strategy' by focusing on enumeration rather than generation or recommendation. However, it doesn't explicitly contrast with 'get_usage_stats' or 'log_execution_feedback', which might also involve listing data.

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 provides clear usage context: 'Use this to understand what frameworks are available and when each is best used.' This gives a specific scenario for when to use the tool. However, it doesn't explicitly state when NOT to use it or name alternatives among the sibling tools, such as using 'recommend_strategy' for selection instead of just listing.

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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