a2a2p — Agent-to-Agent-to-Physical

prepare_domain_solutioning_case

Prepare a synchronous, deterministic, privacy-minimized engineering case for a compatible domain-intelligence system. Use it when intake_classification is capability_concept or assembly_or_system and mechanism selection or decomposition is required. The case preserves requester facts, deterministic evidence, screening state, unknowns, and a versioned candidate-output contract while removing design-file URLs, bytes, contact fields, and callback routes. This operation does not call deb8stack or any model, choose a mechanism, validate a proposal, store a request, contact a supplier, or grant quote, order, payment, or fabrication authority.

Input Schema

TableJSON Schema

Name	Required	Description
`intent`	No	Layer 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.
`contact`	No	Optional email or callback endpoint for quote delivery.
`deadline`	No	Required delivery date or timeframe. Maps to intent.timeline.
`quantity`	No	Number of units needed. Maps to intent.quantity.
`budget_usd`	No	Approximate budget in USD. Maps to intent.budget_envelope.
`constraints`	No	Hard constraints: tolerances, certifications, materials to avoid, size/weight limits. Maps to intent.functional_requirements.
`requirement`	No	Plain-language description of the physical need. Maps to intent.purpose. Include function, dimensions, materials, load/performance requirements, environment, and interfaces where known.
`callback_url`	No	Optional HTTPS URL. When the quote is ready, a2a2p POSTs it as JSON to this URL.
`specification`	No	Layer 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.
`business_context`	No	Optional business requirements (expected volumes, cost targets, ROI constraints). If provided, the quote includes a business case.
`rejected_alternatives`	No	Options already considered and ruled out. Prevents re-suggesting and builds the learning corpus.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`next`	Yes
`safety`	Yes
`contract`	Yes
`authority`	Yes
`activation`	Yes
`provenance`	Yes
`case_digest`	Yes
`requirement`	Yes
`requested_work`	Yes
`expected_result`	Yes
`contract_version`	Yes
`deterministic_evidence`	Yes

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden, and it excels: it discloses that the operation is synchronous and deterministic, preserves specific data categories, removes design-file URLs, bytes, contact fields, and callback routes, and explicitly lists operations it does not perform, such as calling models, choosing mechanisms, storing requests, or granting authority. This gives an agent a strong safety and side-effect profile.

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 dense but every sentence earns its place: purpose, usage conditions, preservation/privacy behavior, and explicit non-actions. It is front-loaded with the primary action and avoids redundant restatement of the tool name.

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?

For a tool with complex nested input, full schema coverage, and an output schema, the description supplies the missing operational context: when to use it, what data is preserved versus stripped, and what the operation will not do. An agent has enough information to decide whether to invoke this tool and to set expectations for its behavior.

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%, so the input schema already documents every parameter thoroughly, satisfying the baseline. The description does not add parameter-level meaning beyond that baseline, though it does clarify that some fields like design-file URLs are stripped from the output case.

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 names a specific verb and resource: it 'prepares' a 'synchronous, deterministic, privacy-minimized engineering case' for a domain-intelligence system. It further distinguishes this from sibling tools by tying it to intake_classification values and mechanism selection/decomposition, so an agent can tell what this tool uniquely produces.

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?

The description explicitly states when to use the tool: 'Use it when intake_classification is capability_concept or assembly_or_system and mechanism selection or decomposition is required.' It does not name specific sibling alternatives or give exclusion conditions, but the stated trigger conditions are sufficiently precise for routing.

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.

prepare_domain_solutioning_case

Input Schema

Output Schema

TDQS

Glama MCP Gateway

TDQS

Resources