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find_recipe

Build an evidence-grounded implementation recipe from your goal, including prerequisites, ordered steps, and validation checks.

Instructions

Assemble an evidence-grounded implementation recipe with explicit prerequisites, ordered steps, and validation steps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesThe implementation goal or workflow to construct a recipe for.
formatNoResponse format: "markdown" (default, human/agent-readable documentation) or "json" (structured raw machine data).
versionNoTarget documentation version.
projectPathNoWorkspace root for project-aware dependency detection.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.2.3
    • addedInput schema / properties / format
      Added value: +{
+  "description": "Response format: \"markdown\" (default, human/agent-readable documentation) or \"json\" (structured raw machine data).",
+  "enum": [
+    "markdown",
+    "json"
+  ],
+  "type": "string"
+}
  2. First observedv0.1.4

TDQS

C2.9/5.0
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 behavioral burden. It hints that results are 'evidence-grounded' but never states that this is a read-only operation, where evidence comes from, or any auth/rate constraints, so the disclosure is thin for a no-annotation 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 that names the action first and then details the output shape. Efficient with no filler, though its dense list makes it slightly run-on.

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?

No output schema exists, but the description does sketch the return structure (prerequisites, ordered steps, validation), which is genuinely useful. It still omits usage context and any behavioral notes, leaving it only minimally adequate for a four-parameter tool.

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% with four fully documented parameters, so the schema does the heavy lifting. The description adds no parameter-level detail (e.g., how 'goal' maps to recipe output), so the baseline 3 applies.

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?

States a specific verb ('Assemble') and resource ('implementation recipe') and elaborates the output structure (prerequisites, ordered steps, validation steps). It does not, however, differentiate itself from related siblings such as find_example, find_api, or get_implementation_context, so a 4 rather than 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 Guidelines2/5

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

The description contains no when-to-use guidance, no prerequisites, and no mention of alternatives. With closely related siblings like find_example and get_implementation_context, the absence of routing guidance leaves the agent to infer selection.

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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