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recommend_cbd_for_me

Recommends three CBD products based on your goal, experience level, and preferences. Input your objective, budget, and any constraints to get tailored suggestions.

Instructions

Recommandation CBD personnalisee. Analyse l'objectif (sommeil, stress, sport...), le niveau d'experience et le budget pour recommander les 3 meilleurs produits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
objectiveYesObjectif principal de l'utilisateur
experienceYesNiveau d'experience avec le CBD
preferred_formatNoFormat de produit prefere
budget_maxNoBudget maximum en euros
constraintsNoContraintes : 'pas de fumee', 'discret', 'bio uniquement', 'vegan'

Implementation Reference

  • Analytics helper logMCPCall() used to log recommend_cbd_for_me calls to the mcp_analytics table in Supabase for conversion tracking.
    export async function logMCPCall(
      toolName: string,
      inputParams: Record<string, unknown>,
      productsReturned: number,
      responseTimeMs: number
    ): Promise<void> {
      try {
        await supabase.from("mcp_analytics").insert({
          tool_name: toolName,
          input_params: inputParams,
          products_returned: productsReturned,
          response_time_ms: responseTimeMs,
        });
      } catch {
        // Silent fail — don't break MCP responses for analytics
      }
    }
Behavior3/5

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

With no annotations, the description carries the transparency burden. It explains the tool analyzes user inputs to recommend 3 products, but does not disclose potential side effects, authentication needs, rate limits, or input validation behavior. Basic but not comprehensive.

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?

Single sentence in French covering the core functionality. Efficient and front-loaded with key information. Slightly lacking structure but appropriate for a simple tool.

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

Completeness2/5

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

No output schema, so description should detail returns. 'Recommander les 3 meilleurs produits' is vague. Missing output structure, usage context, and differentiation from siblings. Requires more context to be fully complete.

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?

Schema description coverage is 100%, so parameters are already documented. The description reiterates objective, experience, and budget but omits preferred_format and constraints. It adds marginal value over the schema without deeper semantics.

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 it provides personalized CBD recommendations based on objective, experience, and budget, outputting the 3 best products. It distinguishes from siblings like search_cbd_products or compare_cbd_products by focusing on recommendation, but lacks explicit differentiation.

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 guidance on when to use this tool versus alternatives like search_cbd_products or compare_cbd_products. The description implies it's for personalized recommendations but does not state when not to use it or which sibling tools serve similar purposes.

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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