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forge_save

Destructive

Save a browser automation tap to disk with automatic git commit. Optionally verify examples to ensure row-shape contracts pass before finalizing.

Instructions

Save the tap to disk as .tap.json and auto-commit to git. Accepts either a structured plan (W3C Annotation wrapping an ExecutionPlan body — preferred) or code (legacy .tap.js source, migrated to a plan on write). Falls back to code from the active forge.draft session when neither is passed. After saving, tap.run can execute it forever with zero AI.

Optional verify_examples runs the just-saved tap against the supplied examples and asserts row-shape contracts (min_rows, non_empty, max_elapsed_ms). When all examples pass, the response includes doctor (verdict from running tap.doctor on the saved tap). When any example fails, the response includes regenerated_inspect — a fresh forge.inspect on the same URL — so the agent can re-forge in one round-trip instead of three. When omitted, behavior is unchanged and the response carries doctor: "indeterminate".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteYes
nameYes
planNoTapAnnotation envelope — W3C Annotation with body:tap:ExecutionPlan. Preferred over code.
codeNoOptional — legacy .tap.js source; auto-migrated to a plan on save
verify_examplesNoOptional auto-verify hook (issue #31). Each example runs the just-saved tap with `args` and asserts the result against `expect`. On any failure the response gains `ok:false` and `regenerated_inspect` (a fresh forge.inspect on the saved URL).
Behavior5/5

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

Annotations indicate destructive hint (true), and description elaborates: 'auto-commit to git', 'migrated to a plan on write', and post-save execution capability. Describes conditional behavior with verify_examples: on success returns doctor, on failure returns regenerated_inspect. No contradiction with annotations.

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?

Description is front-loaded with core action, then modes, then verification details. Each sentence adds value. Slightly lengthy due to detailed verify_examples explanation, but that is necessary for completeness. Could be trimmed slightly without losing clarity.

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?

Given no output schema, the description covers return values thoroughly: doctor on success, regenerated_inspect on failure, and default indeterminate. Explains fallback behavior and migration. Covers all aspects of the tool's behavior, making it self-contained for agent use.

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 coverage is 60%, and description adds meaning for plan, code, and verify_examples with usage details. Site and name are not elaborated in description but are straightforward required identifiers. The explanation of verify_examples' sub-fields (min_rows, non_empty, max_elapsed_ms) compensates for nested complexity. Could describe site/name more, but adequate.

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?

Clearly states the tool's purpose: saving a tap to disk as .tap.json with auto-commit to git. Distinguishes from siblings like forge_draft (which presumably creates drafts) and forge_inspect (which inspects) by focusing on persistence. Mentions two input modes (plan preferred, code legacy) and fallback to draft session.

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?

Explicitly states when to use each parameter: 'plan (W3C Annotation...preferred)' vs 'code (legacy .tap.js source...)', and fallback to active forge.draft session. Also explains when to use verify_examples and its outcomes, enabling the agent to make informed decisions about error handling and round-trip optimization.

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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