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get_technique_guide

Discover Context Engineering techniques for reasoning, workflow, code, or project tasks to identify the best tool for specific needs.

Instructions

Returns a guide to available Context Engineering techniques (The Librarian).
Use this to discover the best tool for a given task.

Args:
    category: Filter by 'reasoning', 'workflow', 'code', 'project', or 'all'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'get_technique_guide' tool, decorated with @mcp.tool() for automatic registration in the FastMCP server. It validates the 'category' input using TechniqueGuideInput and returns a comprehensive markdown guide listing available Context Engineering techniques, categorized by use case.
    @mcp.tool()
    def get_technique_guide(category: str = "all") -> str:
        """
        Returns a guide to available Context Engineering techniques (The Librarian).
        Use this to discover the best tool for a given task.
    
        Args:
            category: Filter by 'reasoning', 'workflow', 'code', 'project', or 'all'.
        """
        try:
            _ = TechniqueGuideInput(category=category)
        except ValidationError as e:
            return f"Input Validation Error: {e}"
    
        guide = """
        # Context Engineering Technique Guide
    
        | Category | Tool | Complexity | Best For |
        |----------|------|------------|----------|
        | **Architect** | `design_context_architecture` | Variable | **Constructor Mode**: Building custom agents/systems. |
        | **Router** | `analyze_task_complexity` | Low | **YOLO Mode**: Finding the right tool automatically. |
        | **Reasoning** | `reasoning.systematic` | High | Complex problems requiring step-by-step logic. |
        | **Reasoning** | `thinking.extended` | Very High | Deep exploration, trade-off analysis, simulation. |
        | **Workflow** | `workflow.test_driven` | High | Implementing features with TDD. |
        | **Code** | `code.analyze` | Medium | Understanding code structure and quality. |
        | **Project** | `project.explore` | Medium | Mapping a new codebase. |
        | **Basic** | `Standard Molecule` | Low | Simple pattern matching (use `get_molecular_template`). |
    
        **Usage:**
        - **YOLO Mode**: Call `analyze_task_complexity` to get a quick tool recommendation.
        - **Constructor Mode**: Call `design_context_architecture` to get a full system blueprint.
        """
        # Simple filtering (mock implementation for now based on description)
        # Ideally, this would filter the text, but the current implementation returns static text.
        # The validation ensures 'category' is safe.
        return guide
  • Pydantic BaseModel defining the input schema for the get_technique_guide tool, enforcing valid categories via regex pattern and providing a default value.
    class TechniqueGuideInput(BaseModel):
        category: str = Field(
            "all",
            pattern="^(reasoning|workflow|code|project|all)$",
            description="Filter category.",
        )
  • The @mcp.tool() decorator line that registers the get_technique_guide function as an MCP tool in the FastMCP server instance 'mcp'.
    @mcp.tool()
Behavior2/5

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 mentions the tool returns a guide and helps discover tools, but lacks details on behavioral traits such as whether it's read-only, if it requires authentication, rate limits, or what the output format entails. The description doesn't contradict annotations, but it's insufficient for a mutation-free tool with no annotation coverage.

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 appropriately sized and front-loaded: it starts with the core purpose, followed by usage guidance, and then parameter details in a clear 'Args:' section. Every sentence earns its place without redundancy or fluff.

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's low complexity (1 parameter, no nested objects) and the presence of an output schema, the description is fairly complete. It covers purpose, usage, and parameter semantics adequately. However, it could improve by addressing behavioral aspects like read-only nature or output expectations, but the output schema mitigates some gaps.

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 description adds meaning beyond the input schema by explaining the 'category' parameter: 'Filter by 'reasoning', 'workflow', 'code', 'project', or 'all'.' This compensates for the 0% schema description coverage. However, it doesn't detail default behavior or other nuances, and with only one parameter, the baseline is 4, but the added value is moderate.

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: 'Returns a guide to available Context Engineering techniques (The Librarian)' and 'Use this to discover the best tool for a given task.' It specifies the resource (guide to techniques) and the verb (returns/discover), but doesn't explicitly differentiate from sibling tools like 'get_cell_protocol' or 'get_molecular_template' which might also provide guides or templates.

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 usage context: 'Use this to discover the best tool for a given task,' suggesting it's for tool selection. However, it doesn't explicitly state when to use this versus alternatives like 'analyze_task_complexity' or 'design_context_architecture,' nor does it provide exclusions or prerequisites for usage.

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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