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Analyze Recipe

analyze_recipe

Deeply analyzes your own recipe to catch issues before publishing, helping ensure it is ready for sharing.

Instructions

Deeply analyze one of the user's own recipes BEFORE publishing it.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "recipe_id": "<recipe_id>"
    },
    "name": "analyze_recipe"
  }
}

Input Schema

NameRequiredDescriptionDefault
languageNoLocale, e.g. "pl-PL", "en-US".en-US
recipe_idYesNumeric id of the user's own recipe to analyze.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries the full burden, and it discloses almost nothing: not whether the operation is read-only, what kinds of problems the analysis surfaces, whether it mutates or costs anything, or any preconditions. "Deeply analyze" is the only behavioral hint, which is too thin for an unannotated tool.

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?

A single front-loaded sentence with no filler and the key constraint (own recipes, before publishing) up front. It is efficient, though so terse that it verges on under-specification rather than optimal conciseness.

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?

An output schema exists, so return values needn't be described. But for a tool with no annotations, the description should still convey what the analysis examines and whether it changes anything; those gaps leave the agent with a partially informed picture.

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 coverage is 100%, so both parameters are fully documented in the schema; the baseline of 3 applies. The description only reinforces the ownership constraint on recipe_id ("the user's own recipes") and adds nothing about the language parameter.

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 states a specific verb+resource ("analyze ... recipes") and narrows scope to "one of the user's own recipes," which distinguishes it from get_recipe and publish_recipe. What the analysis actually produces is left generic ("Deeply analyze"), so it stops short of a 5.

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 phrase "BEFORE publishing it" implies the trigger condition (pre-publish validation), which is genuine guidance. However, no alternatives are named (e.g., get_recipe for simple retrieval) and there are no explicit when-not conditions, so usage is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.