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list_techniques

Discover available prompt engineering techniques with acceptance statistics to enhance AI interactions through optimized method selection.

Instructions

List all 8 available prompt engineering techniques with acceptance stats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler logic for 'list_techniques' which maps over the techniques library and adds acceptance statistics from the profile config.
    case 'list_techniques': {
      const { stats } = getStats();
      return TECHNIQUES.map(t => {
        const s = stats[t.id] ?? { shown: 0, accepted: 0, helpful: 0 };
        const rate = s.shown > 0 ? Math.round((s.accepted / s.shown) * 100) : null;
        return {
          id: t.id,
          name: t.name,
          description: t.description,
          triggers: t.triggers,
          stats: { shown: s.shown, accepted: s.accepted, acceptanceRate: rate },
        };
      });
    }
  • The MCP tool definition (registration) for 'list_techniques'.
    {
      name: 'list_techniques',
      description: 'List all 8 available prompt engineering techniques with acceptance stats.',
      inputSchema: { type: 'object', properties: {} },
    },
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 disclosure burden. It adds valuable context by specifying the fixed count (8) and that acceptance statistics are included, but lacks details on data freshness, caching, authentication requirements, or the specific structure of the returned technique objects.

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 a single, efficient sentence of 9 words. It is front-loaded with the action and resource, and every clause earns its place by conveying scope ('all 8') and content ('acceptance stats').

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

Completeness3/5

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

Given no output schema exists, the description partially compensates by mentioning 'acceptance stats,' hinting at the return value structure. However, for a complete picture without annotations or output schema, it should ideally describe the technique object format (e.g., whether it includes IDs, names, descriptions) to enable proper parsing.

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?

With zero parameters, the baseline score applies per the rubric. The description implies why no parameters are needed (it returns all 8), which aligns with the empty schema. No additional parameter documentation is required or present.

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 provides a specific verb (List), resource (prompt engineering techniques), and scope (all 8 available with acceptance stats). However, it does not explicitly differentiate from sibling tools like 'get_stats' or 'enhance_prompt', which could cause confusion about when to list techniques versus getting general statistics or applying them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance is provided on when to use this tool versus alternatives (e.g., 'get_stats' which might return aggregate data, or 'enhance_prompt' which consumes these techniques). There are no 'when-not-to-use' exclusions or prerequisites mentioned.

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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