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a2a2p — Agent-to-Agent-to-Physical

by com.a2a2p

revise_requirement

Create a new, immutable revision of a prior request by applying only the fields you learned since the first submission. Use this after review_specification returns a clarification_plan: provide the original request_id plus the patch (for example intent.geometric_envelope or specification.material). The original is preserved; the revision receives a new request_id, a fresh spec_review, a revision_of link, and a deterministic readiness_progress comparison against its immediate predecessor. Provide idempotency_key when retrying the same patch so a network retry returns the same revision instead of creating another.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentNoLayer 1 — the problem. What the physical matter needs to DO. Use this for intent-driven requests where the agent describes purpose and a2a2p recommends solutions.
contactNoOptional email or callback endpoint for quote delivery.
deadlineNoRequired delivery date or timeframe. Maps to intent.timeline.
quantityNoNumber of units needed. Maps to intent.quantity.
budget_usdNoApproximate budget in USD. Maps to intent.budget_envelope.
request_idYesThe prior request_id to revise.
constraintsNoHard constraints: tolerances, certifications, materials to avoid, size/weight limits. Maps to intent.functional_requirements.
requirementNoPlain-language description of the physical need. Maps to intent.purpose. Include function, dimensions, materials, load/performance requirements, environment, and interfaces where known.
callback_urlNoOptional HTTPS URL. When the quote is ready, a2a2p POSTs it as JSON to this URL.
revision_noteNoOptional short explanation of what changed.
specificationNoLayer 2 — the solution. What the physical matter IS. Populate what is known. Precise specifications produce faster, tighter quotes. Controlled vocabularies are preferred but open values are accepted.
idempotency_keyNoOptional stable retry key (1–128 characters). Reusing it with the same patch returns the prior revision; reuse with a different patch is rejected.
business_contextNoOptional business requirements (expected volumes, cost targets, ROI constraints). If provided, the quote includes a business case.
rejected_alternativesNoOptions already considered and ruled out. Prevents re-suggesting and builds the learning corpus.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries full behavioral burden and it delivers: immutability, preservation of the original, new request_id, fresh spec_review, revision_of link, deterministic readiness_progress comparison, and idempotency semantics (same patch returns same revision). This goes well beyond the schema.

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?

Three dense sentences front-load the core behavior, then workflow trigger, then side effects and idempotency. No redundant restatement of schema fields or filler.

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

Completeness4/5

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

For a 14-parameter, nested, no-output-schema tool, the description covers the necessary workflow, side effects, and retry behavior. However, it does not describe the exact response payload or error cases (e.g., invalid request_id, empty patch), so an agent has to infer those from schema or runtime.

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%, so baseline is 3. The description adds value by framing parameters as a patch ('applying only the fields you learned'), giving concrete examples (intent.geometric_envelope, specification.material), and clarifying idempotency_key's reuse behavior. These meanings are not fully explicit in the schema.

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?

Opens with a specific verb+object: 'Create a new, immutable revision of a prior request.' It also states the exact trigger ('after review_specification returns a clarification_plan'), which differentiates it from sibling request_physical_solution and the review tool itself.

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

Usage Guidelines4/5

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

Explicitly tells the agent when to invoke it (after review_specification returns a clarification_plan), what to provide (request_id plus patch), and idempotency behavior on retries. It does not explicitly name alternatives or state when not to use it, so it misses the when-not component of a 5.

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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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tool clusters have near-identical names and responsibilities: prepare_derived_beam_simulation, prepare_reviewed_beam_simulation, and prepare_simulation_study all produce bounded simulation studies, while the validate_* family has five variants with subtle input differences. The descriptions are detailed, but an agent would frequently need to read an entire paragraph to avoid misselection.

Naming Consistency5/5

All 24 tools follow the same snake_case verb_noun pattern: build_, check_, request_, validate_, prepare_, run_, upload_, etc. There are no camelCase names, no vague single-word tools, and no stylistic outliers.

Tool Count3/5

24 tools is at the heavy end of the calibration range, and a large subset of rectangular-beam preparation/validation tools could be consolidated. The broad physical-request and supplier pipeline justifies some of the count, but the overall surface still feels over-scoped.

Completeness4/5

The core workflows are covered: upload, submit, revise, status, spec review, pricing/estimates, quote-job polling, supplier package/email rendering, and a full bounded simulation loop. Missing cancellation, request listing, and actual supplier send/order actions are real but peripheral gaps rather than workflow-killing dead ends.

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