a2a2p — Agent-to-Agent-to-Physical

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

With no annotations, the description carries full burden. It discloses the asynchronous aspect ('once ready') and enumerates the full report contents, which is transparent. It does not explicitly state it is read-only, but 'check status' clearly implies no mutation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose. The second sentence lists return contents in a compact, scannable way. Every word earns its place, with no fluff or repetition.

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

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

The tool is simple (1 parameter, no output schema), and the description thoroughly covers return values and async behavior. It explains the full report contents, making it sufficient for an agent to understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds no new detail about request_id beyond the schema, which already explains it comes from request_physical_solution. No additional semantic value is provided.

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

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

The description clearly states the tool's function with a specific verb and resource: 'Check the status of a previously submitted physical-solution request.' This distinguishes it from siblings like request_physical_solution (submission), get_supply_options (options), and review_specification (spec review).

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

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

The phrase 'previously submitted' implies usage after request_physical_solution, and the schema explicitly references that tool's request_id. However, there is no explicit exclusion or mention of alternatives, only implied context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It discloses synchronous execution, non-persistence ('nothing is submitted or stored'), source labeling ('clearly labeled, never presented as a quote'), and mesh measurement specifics. It does not cover auth, rate limits, or failure modes, but the core behavioral disclosures are strong.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph, but every sentence adds value: purpose, source behavior, covered processes, STL guidance, fallback input, and usage context. It is front-loaded with the core purpose and has no fluff, though structure could be improved with bullets.

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

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

For a tool with 11 optional parameters and no output schema, the description provides a clear minimal invocation recipe (STL or geometric envelope + quantity + material) and explains the output concept (pricing/lead time with source label). It doesn't detail every optional field, but those are self-explanatory in the schema, so completeness is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds practical semantics beyond the schema: 'ATTACH AN STL in specification.design_files and a2a2p measures the mesh directly... Otherwise supply intent.geometric_envelope (bounding box in mm), quantity, and material.' This tells agents the optimal input path and fallback, which the schema alone does not convey.

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

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

The description clearly states the tool's function: 'Instant, synchronous pricing and lead time for a physical requirement.' It specifies a concrete resource (supply options) and distinct behavior (quotes vs. parametric estimates). It differentiates from siblings like check_request_status and request_physical_solution by focusing on pricing/lead time.

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

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

Provides explicit when-to-use: 'Use this to answer "what will this cost and how long will it take?" inside a single session, before committing to a full resolution report.' This clearly implies placement in the workflow. However, it does not name alternative tools or explicitly state when not to use, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden, and it delivers: it discloses asynchronous behavior (immediate request_id + instant spec_review, draft in minutes, final within 24 hours), the contents of the resolution report, and the need to poll `check_request_status`. This goes well beyond basic 'submit a request.'

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well structured with purposeful headings and front-loaded purpose. Each section earns its place: overview, async/retrieval, entry modes, and layer definitions. The length is justified by the tool's complexity (11 parameters, nested objects, multiple entry modes).

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

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

Despite no output schema, the description explains what the response contains (RESOLUTION REPORT with pricing, tradeoffs, delivery plan, business case) and how to retrieve it. It also guides the caller through partial-vs-structured inputs, making the tool actionable even with minimal upfront information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the two 'layers' (intent vs specification), the minimum requirement ('requirement' or 'intent.purpose'), and the entry-mode strategies. It does not detail every parameter individually, but the schema already handles those, and the description's layer model clarifies how the many parameters relate.

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

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

The description opens with a specific verb and object: 'Submit a physical-world requirement (a part, product, device, or capability that must exist in the physical world).' It clearly states what the tool does and what it returns (a RESOLUTION REPORT), and it distinguishes itself from the sibling polling tool by referencing `check_request_status` for retrieval.

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

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

The description provides strong usage guidance through ENTRY MODES (fully_specified, intent_only, partial), explains when to use each, and instructs to use 'rejected_alternatives' to record ruled-out options. It also points to `check_request_status` for polling. However, it does not explicitly contrast with `get_supply_options` or `review_specification`.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description bears full responsibility for behavioral disclosure. It clearly states 'nothing is submitted or stored' (side-effect-free), 'Instant, synchronous' (timing), and the return format ('ranked blocker/warning/info plus a quote_readiness score'). It also reveals the duplication behavior in request_physical_solution, which prevents surprising the agent with a repeated review.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence carries unique value: it front-loads the core purpose, enumerates specific checks, describes returns, states input compatibility, and gives an iteration loop. There is no filler or repetition; it is as concise as possible while being comprehensive.

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

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

Given the tool's complexity (11 parameters, nested objects, no output schema), the description covers all essential aspects: synchronous behavior, no side effects, the full list of checks, return format, input modes, and how it fits into the broader workflow. It leaves no ambiguity about what the tool does or when to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful input-format guidance: 'Accepts the same input as request_physical_solution (structured intent/specification or legacy flat fields),' which clarifies that the tool supports two input styles and enables reuse of same parameter structures across tools. This additional context elevates the score above baseline.

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

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

The description clearly states the tool's function as an 'Instant, synchronous engineering review of a physical requirement,' listing specific deterministic checks, and explicitly distinguishes it from siblings by outlining its role before request_physical_solution. It mentions accepting the same input as request_physical_solution, further clarifying its purpose in the workflow.

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

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

The description provides explicit usage workflow: 'Iterate: review → tighten the specification → review again until quote_ready, then submit with request_physical_solution.' It also notes that request_physical_solution runs the same review, thereby guiding the agent on when to use this tool and when the review is embedded in another tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.