Fabryka Haftu Opole – Patch Quoting

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

While annotations cover technical safety (readOnlyHint: true), the description adds essential operational behavioral traits—namely, that the output is informational and lacks production guarantees. This is vital context for an agent interacting with real-world manufacturing logistics.

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 two sentences are highly efficient: the first states the tool's utility (what it checks), and the second states its limitations (what it does not confirm). No sentence is superfluous.

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 that it is a read-only, zero-parameter tool with clear operational constraints defined in the description, the definition is highly complete for an AI agent to use it responsibly.

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?

Since there are zero parameters, the description cannot add semantic meaning beyond what is already implied by the zero-parameter input schema. The baseline score of 4 is appropriate for this scenario.

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 defines the tool's function as an information retrieval mechanism regarding requirements, minimum orders, and quoting process. This distinguishes it clearly from sibling tools like 'submit_patch_inquiry'.

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?

It explicitly states crucial limitations by declaring that it does not confirm production availability or deadlines. This serves as a strong usage guideline, preventing the agent from making assumptions based on the tool's output.

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?

Beyond the annotations it discloses latency (analysis may take several minutes), persistence (the original graphic and quote are saved on the server), commercial limits (indicative quote, excludes delivery, no order placed, no deadline guaranteed), and the human-review flags (manual_review_required / manual_review_available / project_saved). This is materially more than the readOnlyHint=false / openWorldHint=true annotations convey.

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 core purpose and required inputs are front-loaded, then image handling, then limits and the follow-up workflow, which is a sensible order. It is dense and slightly repetitive on the anti-hallucination rules (local path/identifier warning restated as 'don't invent files, URLs, identifiers, base64'), but almost every sentence carries operational weight for a multi-step tool.

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 six-parameter tool with a nested object, an output schema, and a downstream handoff, the description covers the full lifecycle: valid input construction, attachment failure recovery, fallback channel, and post-call routing. With an output schema present, it correctly does not waste space describing return shapes, only the flag semantics the agent must act on.

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?

The schema does not express mutual exclusivity among the image sources, and the description supplies that critical rule: exactly one non-empty image source, omit the others, no local paths or bare identifiers in image_file/image_url, and a one-time retry with image_base64 if the attachment fails to arrive. It also gives concrete examples for size and quantity, which compensates for the 67% schema coverage on those fields.

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 resource: it analyzes one project and prices embroidered/sublimated patches. It also names the sibling it hands off to (submit_patch_inquiry), so an agent can distinguish it from get_patch_capabilities and submit_patch_inquiry without reading the schemas.

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?

It states exactly when to call it (one design plus size/quantity/backing/real graphic), when to fall back to the fabrykahaftu.com.pl/chatgpt link (only if the real file is truly unavailable), and when and how to escalate to submit_patch_inquiry with purpose=manual_review after project_saved=true. It also says what not to do (do not invent files/URLs/base64, do not re-add the graphic, do not tell the user to click a ChatGPT button).

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?

Goes well beyond the annotations: discloses SMTP email sending, publishing into the analyzer's push module, the meaning of email_sent and push_queued (queued ≠ delivered), that a repeat call won't resend a confirmed email but may fill a missing push, and that it neither orders, charges, nor guarantees production. It even warns against claiming success when email_sent=false.

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?

Usage conditions are front-loaded, which helps, but the text is dense and repeats itself: 'Nie wymagaj ponownego przesyłania grafiki' and 'Grafika i parametry są już zapisane; nie przesyłaj ich ponownie' say the same thing, and the order_form/manual_review rules are interleaved with delivery semantics in one long run-on stream.

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?

Covers everything an agent needs before calling: prerequisite state from quote_patch, per-purpose preconditions, side effects (SMTP + push), result semantics, and idempotency behavior. The output schema exists and the description still explains the two status fields, so nothing relevant is missing.

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 earns extra by adding cross-parameter rules the schema lacks — reuse only the quote_id and inquiry_token from that specific quote response, use haft for manual_review, and never derive email/consent from analysis_text or the image. It does not add much on email format or length limits, but those are already in the schema.

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?

States a specific action on a specific resource: it saves an inquiry ('Zapisuje zgłoszenie i powiadamia firmę') and routes it either to an order form or to a graphic-designer review. It also distinguishes itself from the quoting sibling by requiring quote_id/inquiry_token from quote_patch. It is clear, though the core purpose is buried under conditional instructions rather than stated up front.

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?

Explicit branching: purpose=manual_review with technology=haft only when the client supplied an email, consented to forwarding, and the project needs a graphic assessment; otherwise purpose=order_form. It also states hard exclusions — don't demand the graphic again, don't invent ChatGPT buttons, don't read the address or consent from analysis_text.

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