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Gate an implementation plan

validate_plan
Read-onlyIdempotent

Review implementation plans and specs before coding to catch design flaws early. Returns a binary verdict covering completeness, feasibility, performance, security, and missing considerations.

Instructions

The gate for PLANS, designs and specs — run BEFORE any code is written, the cheapest place to catch a bad design. Returns the same binary verdict shape as validate_ai_output, with findings on completeness, feasibility, performance and scalability implications, security risks and missing considerations (e.g. it rejects a plan that polls an API every 100ms per client, with the request-volume math). Read-only: nothing is executed or stored beyond the verdict. Use validate_ai_output for the code that follows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
planYesThe implementation plan, design or spec to validate, as plain text or markdown — e.g. a numbered migration plan or an architecture sketch.
contextNoOptional constraints the review should weigh, e.g. {"system": "payments API", "scale": "1M users", "constraints": "PostgreSQL only"}.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoreYesCombined score 0-100 (gates fused with the model review)
validYesThe binary verdict: true = approved, false = rejected
issuesYesSeverity-ranked findings, each 'severity|category|detail' with the reasoning
providerNoModel that ran the deep review, or 'protection-gates-only'
confidenceNoscore / 100
protectionNoDeterministic gate result. A veto here is final — the model review cannot override it.
suggestionsNoConcrete fixes to reach approval
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description reinforces and extends this by stating 'Read-only: nothing is executed or stored beyond the verdict' and adding evaluative details (completeness, feasibility, performance, security) plus a concrete example of a rejected plan. It provides context beyond the annotations without contradiction.

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 compact (four sentences), front-loaded with the primary purpose, then adds behavior, side effects, and an alternative tool. Every sentence adds relevant information with no redundancy or fluff.

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

Completeness5/5

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

Combined with the rich annotations, full parameter schema, and output schema, the description covers purpose, usage timing, behavior, side effects, and alternatives. It does not need to explain return structure because output schema exists. It is complete for a validation tool of this complexity.

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?

Schema description coverage is 100% for both 'plan' and 'context', so baseline is 3. The description adds value by giving a concrete rejection example (polling every 100ms) and enumerating evaluation criteria (completeness, feasibility, etc.), which helps the agent craft an appropriate plan. This goes slightly beyond schema descriptions, justifying a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a gate for PLANS, designs, and specs, with a specific verb implied ('run BEFORE any code is written'). It distinguishes itself from validate_ai_output by explicitly stating that validate_ai_output is for the code that follows, making the resource and timing unambiguous.

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

Usage Guidelines5/5

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

The description provides explicit timing ('BEFORE any code is written'), positions it as the 'cheapest place to catch a bad design,' and directly names the alternative for code validation ('Use validate_ai_output for the code that follows'). This gives the agent clear when-to-use and when-not-to-use guidance.

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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