Agentic Fabrication Network (AFN)

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the secure payment page collects the shipping address/email, that the tool returns a checkout_url, and that UFP places the vendor order automatically after payment — a clear side-effect disclosure for a mutation tool. It could mention auth/error cases, but the core behaviors are transparent and complete.

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?

Five sentences, no wasted words. The core purpose and timing are front-loaded, followed by the address caveat, the return value, and the post-payment behavior. Every sentence earns its place and the structure is easy to scan for an 18-parameter 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 complex 18-param tool with no output schema, the description ties the workflow together: when to call, what to pass, what comes back (checkout_url), and what happens after payment. It lacks a detailed shape of the response beyond the URL, but the essential integration logic an agent needs is all present. Nearly complete.

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 50%, so the description must compensate for the uncovered half. It adds valuable guidance on the address-field philosophy ('Only pass address fields if the user already volunteered them') and implicit rules for return_url ('never invent one'). The schema itself documents the other half (name, email, coupon_code, billing_name, shipping_option_id). The description complements rather than fully compensates for the coverage gap, leaving some params (zip, city, phone, street1) to inference.

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 + resource + outcome: 'Create the order and get a hosted payment link for a chosen offer.' It clearly distinguishes from siblings like get_order_status, list_orders, and leave_review, none of which create a checkout or issue a payment link. An agent can immediately tell what this tool does and when it applies.

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 timing guidance is given: 'Call this as soon as the user picks an offer + shipping speed — do NOT ask for their address first.' It also states when to pass address fields ('only if the user already volunteered them'). It would earn a 5 by naming an alternative tool to use instead under certain conditions, but the workflow context it provides is concrete and actionable.

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 and meets it: discloses a ~40s blocking behavior, the need for immediate invocation, and the lack of added value if board is already populated. It also implies the tool is safe to call without side effects, though not explicitly stating read-only.

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?

Three dense sentences, each essential: condition+action, execution instructions, and a critical exclusion. Information is front-loaded with the primary purpose and constraint. No filler 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?

Despite no output schema, the description explains the return value ('full board of offers'), the expected wait time, and the exact preconditions. It is sufficiently complete for a single-parameter tool with clear behavior, and it positions itself against sibling tools without needing further context.

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 already provides 100% coverage with 'Quote id from the still-quoting response', so the baseline is 3. The description adds no substantive parameter meaning beyond reinforcing 'just quote_id' and 'immediately', which are usage directives rather than semantic details.

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 uses a specific verb ('waits for... and returns') and clearly identifies the resource ('vendors' offers') and the trigger (empty board response). It sharply distinguishes from siblings by specifying the exact condition under which this tool is used, unlike generic 'finalize' naming.

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?

Explicitly states when to use (only after an empty board response and told to call it), how to use (call immediately, with just quote_id, say nothing substantive), and when NOT to use (never on a response that already listed offers). This provides clear decision-making rules and practical timing guidance.

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 provided, the description carries the full burden, and it does so thoroughly: it discloses auto-conversion of file formats, UFP saved-intent behavior, substitution notes, vendor minimums, default shipping assumptions, and explicit prohibitions like 'NEVER guess or invent a ZIP.' This far exceeds the baseline for a quote tool.

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 appropriately sized for a highly parameterized tool. It is front-loaded with the core purpose, then process routing, UFP behavior, input methods, and reorder rules. Every sentence earns its place; there is no filler.

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 47 parameters, no required parameters, no output schema, and no annotations, the description is remarkably complete. It covers return shape (price, ETA, preview image), design input routes, reorders, sharing constraints, and process-specific file handling, so an agent has enough context to invoke the tool correctly in most scenarios.

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%, yet the description and schema parameter docs still add meaningful semantics: process-specific file type mappings, what design_file versus file_url means, reorder semantics via part_number, and locked-part share_key requirements. The description goes well beyond the schema's structural definitions by explaining real-world usage context.

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 first sentence states a specific verb and resource: 'Get ranked, purchasible offers (price, ETA, preview image) for fabricating a physical item from a design file.' This clearly differentiates from siblings like poll_quote, preview_quote, and finalize_quote by emphasizing the initial quote-fetching behavior with purchasable, ranked offers.

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 gives explicit when-to-use and when-to-omit guidance: omit process on bare file drops, use design_file over file_url, use part_number instead of any file for reorders, and require share_key for locked parts. This is actionable routing guidance that helps an agent select and invoke the tool correctly without guessing.

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 fully discloses behavior: returns caveats-first facts, tags sources (vendor-cited vs general), provides strength ladders, and explicitly states it never gives safety guarantees. This is comprehensive transparency.

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 detailed but well-structured, front-loading the core purpose. It is slightly long, but every sentence adds essential information. Could be tightened, but still efficient for the complexity.

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 no output schema, the description thoroughly explains what is returned. It covers all relevant aspects for a 3-parameter tool, including what the tool does and its limitations. Complete for its purpose.

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% with all parameters documented. The description adds value by explaining how 'question' is used to mine for intent, but does not significantly add beyond schema for 'process' and 'material'. Thus slightly 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 provides material intelligence for specific questions (strength, durability, heat, outdoor). It lists concrete outputs like published specs, comparative ladders, and finish options. The purpose is distinct from sibling tools which handle orders, quotes, and reviews.

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 explicitly instructs to call the tool BEFORE answering material questions and to relay facts. It sets boundaries by stating it supplies facts only, not fitness or safety guarantees. While it doesn't mention alternatives, sibling tools are unrelated, so no confusion.

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 behavioral burden, and it pays off: it discloses that this is live state that changes as connectors join/leave or capability branches toggle, that the process parameter is advisory in v1, and that unspecified lengths default to the most-stocked length. These are non-obvious behaviors that meaningfully affect invocation and interpretation.

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 dense, front-loaded paragraph that communicates important scoping and behavioral details without irrelevant filler. It is longer than strictly necessary, but the detail is substantive and helps the agent understand a complex, information-rich 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?

Given the tool has only one optional parameter and no output schema, the description covers the return surface well: services per shop, size/material/thickness windows, gauge ladder, stocked hardware combinations, finish color menus, and manufacturability rules. It also covers dynamic behavior and usage context, so an agent has enough to invoke and interpret results correctly.

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 single parameter is 100% covered by the schema, which already states it is advisory and that v1 returns the whole catalog either way. The description reinforces this but does not add new parameter-level meaning beyond 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?

The description names a specific resource (the live fabrication network capability catalog) and a clear verb (returns/retrieves), with an explicit contrast to quote tools. It enumerates the major content areas (services ledger, gauge ladder, hardware combos, finishes, manufacturability rules), making it easy to distinguish from get_fabrication_quote and get_material_guide.

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 explicitly states when to call: whenever the user asks what's possible in materials, gauges, hardware, colors, or services, and not from memory. It also signals a key exclusion—'no quote needed'—implying that for actual quotations the user should use the quote tools. This is strong, actionable guidance.

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?

No annotations are present, so the description carries the full burden. It discloses that this is a read operation returning status, ETA, and tracking, but lacks details on response structure, potential errors, or required permissions. Adequate for a simple read tool, but not rich.

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 entire description is one concise sentence that front-loads the action and key data points. No unnecessary words 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?

For a simple single-parameter lookup, the description covers the key return values (status, ETA, tracking). It does not explain how to obtain order_id, but sibling tools like list_orders likely provide that. The lack of an output schema is partially compensated by listing return items.

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 has only one parameter, order_id, with no description coverage. The parameter name is self-explanatory, and the description references 'a UFP order,' but does not explain the format or source of order_id. Minimal addition beyond the schema, but the parameter is unambiguous.

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 checks status, ETA, and tracking for a single UFP order. The verb 'check' is specific, and the resource scope distinguishes it from sibling tools like list_orders.

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 implies it is for a single order's status, but does not explicitly state when to use it instead of alternatives like list_orders. No exclusions or when-not guidance is provided.

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?

The description discloses that the tool structures feedback into stars/facets, updates existing reviews on resubmission, and requires user confirmation. It also warns against embellishing comments. With no annotations provided, the description fully carries the burden of behavioral disclosure.

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 somewhat long but every sentence serves a purpose: purpose, structuring rule, confirmation requirement, update behavior, and reference to list_orders. It's dense but not wasteful, with no redundancy.

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 no annotations and no output schema, the description covers purpose, process, facet mapping, confirmation step, and update semantics. It adequately equips an agent to invoke it correctly, including handling ambiguous order references.

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 already provides 100% coverage with descriptions for all parameters. The description adds value by mapping user phrases to facet scores ('the cut was sloppy' -> quality) and clarifying comment brevity. This goes beyond the schema's parameter descriptions.

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 records a verified-purchase review for a UFP order, with a specific verb ('record') and resource ('review'). It distinguishes from siblings like list_orders or get_order_status by focusing on submitting feedback.

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 instructs to confirm the drafted review with the user before calling, and to use list_orders first to resolve which order. It also notes one review per order with update semantics. While it doesn't list alternatives or exclusions, these guidelines are clear and actionable.

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 provided, the description carries the full burden. It discloses the ordering behavior (newest first) and the returned fields. It does not explicitly state it is read-only, but the action of listing without side effects is implied. This is sufficient for a list tool, though a note about no state change would have been more transparent.

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 long, front-loaded with the main purpose, and includes a concrete example of usage. Every sentence provides value without redundancy.

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 no input parameters and no output schema, the description covers the essential return fields, ordering, and a practical use case. It also differentiates from sibling tools, making the context complete for an agent to select and invoke correctly.

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 tool has zero parameters, and the schema coverage is 100% by default. Per rubric, a baseline of 4 is appropriate since there are no parameters to explain and the description adds no param-specific semantics needed.

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 lists the agent's recent UFP orders with a specific set of fields and sorting (newest first). It also explicitly distinguishes its role from siblings by noting it resolves vague references before calling get_order_status or leave_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 description gives explicit when-to-use guidance: to resolve vague order references into a concrete order_id. It also names specific alternative tools (get_order_status, leave_review) and indicates this tool should be used before them, providing clear context and alternatives.

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 for behavioral disclosure. It does reveal that the tool is internal and that the board refreshes itself, and it warns that the board is already final. However, it does not specify what happens if the tool is called manually (errors, side effects, return value), leaving some ambiguity about actual runtime behavior.

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 concise sentences that front-load the INTERNAL warning and immediately give actionable prohibitions. Every clause earns its place, with no filler or repeated schema details.

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 this is an intentional internal-only tool with a single well-described parameter and no output schema, the description covers the essential context: what the tool is for, who calls it, and why the agent must not call it. It lacks explicit return/error details, but for a tool the agent is explicitly forbidden from invoking, the guardrails are sufficiently complete.

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 input schema fully describes quote_id as 'Quote id the board is watching' with 100% coverage, so the description adds no additional parameter semantics. The baseline of 3 applies because the schema already handles the parameter information.

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 that the tool is an internal callback used by the offer board to refresh itself while vendors finish pricing, which goes beyond the title. It also distinguishes itself from refine_quote, warning not to use that tool for re-inspection because it paints a duplicate board.

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 gives explicit, non-ambiguous usage rules: 'Never call this yourself' and explicitly warns against using refine_quote to look at the board again. It explains that the provided board is already final for its turn, so calling this tool is unnecessary.

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?

No annotations exist, and the description carries the behavioral burden by clearly disclosing side-effect-freedom: 'NOTHING committed', no child quote, no vendor ask, no demand logging. It also discloses a limitation ('lanes can still decline once asked') and a non-behavior ('an expectation, never a promise'). A minor gap is lack of clarity on output shape or pagination, but the core behavioral contract is explicit.

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 compact and front-loaded. The first sentence states the purpose, scope, and key guarantee. The comparison with refine_quote and the 'Never call this' warning are efficient. It could be slightly shorter, but it earns its place by resolving ambiguity with the sibling.

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 33 parameters, no annotations, no output schema, and a high-risk sibling (refine_quote), the description is nearly complete. It covers the key decision criterion (preview vs commit), the behavioral boundaries, and the relationship to refine_quote. However, with no output schema, the description could still mention what the response returns (e.g., lanes, exclusion notes) more explicitly; it mentions 'how many distinct fabricators (and which process lanes)' and 'exclusion notes' but not the full return envelope.

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 high at 82%, so the schema already documents most parameters. The description adds crucial guidance: it frames parameters as 'same arguments as refine_quote', clarifies the 'nothing committed' semantics, and highlights that price-related parameters are absent because it carries no prices. This adds meaningful selection-level meaning.

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 identifies the tool as the routing half of refine_quote with a clear non-commitment guarantee, and explicitly contrasts it with refine_quote. It also states what it answers (distinct fabricators, process lanes, exclusion notes) and what it never does (create child quote, ask vendors, log demand).

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 says when to use it ('to preview routing') and when not to ('Never call this to look at prices'), and names the alternative ('Use refine_quote to actually re-price'). This is a clear when/when-not/alternative directive.

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 provided, the description carries full behavioral disclosure and delivers: it explains what re-prices, what returns instantly without re-pricing, that undo restores stored state exactly, that scenarios cap at 4, and that unmatched/unknown inputs are reported rather than errors. It also covers side effects like baseline resets and requirement-removal semantics.

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 content is front-loaded and every major sentence carries decision-critical routing information. However, it is a dense single block of text with some redundancy against the per-parameter schema descriptions, so it is strong but not maximally concise.

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 33 parameters, no annotations, and no output schema, the description is remarkably complete: it covers call modes, undo/clear semantics, scenario and sort behavior, filter inclusion semantics, and edge cases like unmatched vendor names and unresolvable requirements. Nothing needed to invoke it correctly appears to be 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?

Even with 82% schema coverage, the description adds substantial mapping value: user phrasings are tied to arguments ('only US vendors' → ships_from, 'sheet metal' → processes), and it adds rules the schema alone does not convey — never guess a ZIP, don't pass material changes as requirements terms, colored finish should set both finish and color, and remove_requirements is the only removal channel.

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 precise verb and subject — 'Narrow an existing quote' — then enumerates the full action space: constraint narrowing, spec changes, scenario comparisons, sorting, undo/clear, and quote re-reads. This makes it clear refine_quote is the mutation/refinement tool for an existing quote rather than a new-quote or checkout tool.

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 gives excellent internal routing rules: narrowing vs spec change, sort_by vs sort_order, scenario labels for comparisons, and undo_last_step sent alone. It does not explicitly name sibling tools as alternatives or exclusions, so an agent gets clear context but not full cross-tool guidance.

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. It discloses that the message is relayed via the network account, that contact details and links are stripped, and warns against including them. This is significant behavioral context beyond a simple 'send'.

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?

Two sentences, front-loaded with purpose, then usage guidance. Every clause adds value: the flow reference, the confirmation step, and the network sanitization warning. No wasted words.

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 absence of output schema, the description still provides enough context: when to use, how to use, and a critical behavioral caveat. It is complete for a two-parameter send action with strong usage guidelines.

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 schema already documents both parameters. The description reinforces that message is in the user's words and relayed, but adds little beyond what the schema provides.

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 sends the user's answer to a maker's question about a UFP order. It references get_order_status, distinguishing this as the complementary action to surfacing the question.

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?

Explicitly instructs to confirm the answer with the user before sending and ties usage to get_order_status surfacing an open question. Also provides a clear exclusion: never include emails, phone numbers, or URLs the answer depends on.

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