printing-labs

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

With no annotations, the description carries full burden. It discloses side effects: creates a calendar event with Meet link unless Phone Call, registers the meeting, and sends emails. It also adds the consent requirement. It does not cover failure modes or reversibility, but the disclosed behaviors are sufficient for an agent to understand the tool's impact.

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 three sentences, front-loaded with the core action and side effects, then prerequisites and consent. Each sentence contributes essential information with no redundancy or filler, making it concise and well-structured.

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 (9 parameters, 5 required) and absence of an output schema, the description adequately covers the process side effects and prerequisites. It does not describe return values, but with schema covering parameters and the description covering behavior, it is sufficiently complete for correct selection and invocation.

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 meaningful context: the mode affects whether a Meet link is created, and time must be from get-meeting-slots (already in schema, but reinforced). This extra behavioral nuance about mode is not in the schema, slightly increasing the value.

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 'Book' with a clear resource 'consultation with the Printing Labs team' and lists concrete outcomes (Google Calendar event, registration, emails). It clearly distinguishes this from sibling tools, especially get-meeting-slots, by describing the booking action and its side effects.

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 prerequisites: the time must come from get-meeting-slots, and the agent must have user consent and real contact details. It clarifies the sequential relationship with get-meeting-slots. However, it does not explicitly state when not to use the tool or mention alternative tools, so it falls 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 does so well. It discloses the computation steps (quantity-discount tiers, GST), validation of minimum order quantity, that the next discount tier is shown, and the limitations of indicative pricing. This gives the agent a solid model of what the tool does and doesn't do.

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 the primary action, and every clause adds information: computation method, validation, next-tier output, and a caveat. No filler or 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?

Despite having no output schema, the description adequately communicates what the tool returns: effective price, next discount tier, and validation status. It covers the main behavioral aspects (tiers, GST, MOQ, indicative pricing) and is sufficient for an agent to select and invoke the tool 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?

Since schema description coverage is 100% and both parameters are well-described, a baseline of 3 is appropriate. The description adds some context by mentioning quantity-discount tiers and MOQ validation, which slightly enriches the 'quantity' parameter, but it doesn't add substantial parameter-level details 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 opens with a specific verb ('Compute') and resource ('effective bulk price') and clearly identifies the inputs (product and quantity). It distinguishes itself from siblings like search-products and request-quote by focusing on pricing calculation rather than product discovery or final quoting.

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 when to use the tool (to get an indicative bulk price) and explicitly warns that final quotes come from the sales team, effectively steering users away from using this for binding quotes. It doesn't explicitly name alternatives, but the context of siblings like request-quote makes the distinction clear.

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 provided, so the description carries full behavioral burden. It transparently discloses that the tool removes the Google Calendar event and notifies both sides — clear side effects. It also discloses the token requirement as a behavioral dependency. Slight gap: it doesn't state error behavior when the token is invalid/expired or whether cancellation is irreversible.

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 concise, front-loaded sentences with zero wasted words. The first sentence immediately states the action and prerequisite, the second ties the token to a user-facing artifact, and the third states the side effects. Excellent information density.

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 single-parameter tool with 100% schema coverage and no output schema, the description covers the essential behavior: what it does, prerequisites, and side effects. It could add note about failure modes or the manageToken's status requirement, but for the tool's limited complexity this is reasonably 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 100% and the manageToken parameter's schema already documents its meaning. The description adds useful context by explaining the token's origin (from book-meeting) and its role (powers the cancel link), but this primarily re-emphasizes what the schema states rather than adding entirely new semantics.

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+resource ('Cancel a meeting you booked'), clearly scoping the action to bookings made 'earlier in this conversation.' It identifies the key prerequisite (manageToken from book-meeting) and distinguishes the operation from the sibling reschedule-meeting by focusing specifically on cancellation.

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?

States explicit context: only meetings booked in this conversation can be cancelled, and explicitly requires the manageToken, tying it to the cancel link in the customer's confirmation email. Though it doesn't name alternatives like reschedule-meeting, the 'you booked earlier in this conversation' scoping gives clear when-to-use guidance and implies the token requirement is a hard precondition.

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 of disclosure. It mentions the API key requirement, the creation side effects (email invites), the return values (claim + HR dashboard links), and the fulfillment flow. It omits details on reversibility or idempotency, but primary behaviors are 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 dense sentences, front-loaded with purpose and auth, and conveys all necessary high-level information without 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 no output schema and no annotations, the description covers the purpose, authentication, flow, and return values. It could mention edge cases or failure modes, but for a creation tool with a clear goal, it is reasonably 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 has 100% coverage with descriptive property definitions, so the baseline is 3. The description adds minimal extra meaning beyond the schema (e.g., 'chosen kit' maps to kitProductSlug). No compensation 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 a specific verb+resource: 'Creates a Swag Drop for the chosen kit, invites each employee by email, and returns the claim + HR dashboard links.' This uniquely identifies the tool's function and distinguishes it from siblings like request-quote or track-order.

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 clear context for use: 'turn new hires into welcome-kit claim links in one call' and 'HR/HRIS automation.' It doesn't explicitly mention alternatives or exclusions, but the context is strong enough to infer when this tool is applicable.

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 burden of disclosure. It transparently explains the fee structure (setup + monthly plans) and cautions about the distinction between platform fees and merchandise pricing. It doesn't detail return format, but the content is sufficient for a simple read-only lookup with no side effects.

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 concise and well-structured: it opens with the main purpose, then clarifies scope, and ends with a critical usage warning. Every sentence earns its place, with no filler or 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 the tool's simplicity (no parameters, no output schema), the description is complete: it covers what the tool returns, clarifies that these are platform fees, and explicitly references the sibling calculate-bulk-price for per-item pricing. This is sufficient for an agent to select and invoke the tool 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, so parameter semantics are not applicable. The baseline of 4 is appropriate; the description avoids adding unnecessary parameter details and instead focuses on the meaning of the returned cost data.

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 returns Brand Store costs: one-time setup fee and monthly plans (seats, features, price). It explicitly distinguishes this from per-product pricing via calculate-bulk-price, making its purpose unmistakable.

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 guidance on when to use this tool (for platform/storefront fees) and when not to (for per-item merchandise pricing, which belongs to calculate-bulk-price). It also warns never to present a plan price as a per-item price, preventing misuse.

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 must carry the behavioral disclosure burden. It implies the data is curated ('verified facts') but does not state whether the operation is read-only, how the data is returned, or any limitations. The description adds little beyond content categories, leaving safety and execution behavior opaque.

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 sentence that front-loads the core purpose ('Verified facts about Printing Labs') and then lists covered topics efficiently. It is concise without excessive length, though the comma-separated list could be slightly overwhelming.

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 no-parameter tool, the description gives a reasonable overview of content scope. However, it does not clarify the output format or structure (e.g., JSON fields, plain text) or any service-specific behavior. Since there is no output schema, describing the return value would improve completeness.

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, so the description need not explain input semantics. Per the baseline for no-parameter tools, a score of 4 is appropriate; there is no schema information to contradict.

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 identifies the tool as providing verified facts about Printing Labs, enumerating specific content areas like services, fulfillment regions, and contact info. This distinguishes it from product-focused siblings like get-product-details and list-catalogs, though it lacks an explicit verb beyond the tool name.

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 no explicit guidance on when to use this tool versus siblings. It merely lists content categories, leaving the agent to infer that this is for general company information. No exclusions or alternative tool mentions are 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context by noting that slots reflect the live calendar, implying real-time data freshness. While it doesn't explicitly state that the operation is read-only or describe return format, the verb 'get' and the context strongly imply a non-mutating lookup, and the description is transparent enough for a simple read 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 a single, well-crafted sentence that packs the essential information: purpose, team, time zone, and usage context. It is front-loaded with the core function and avoids redundancy, making it highly efficient.

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 one-parameter tool with no output schema, the description adequately covers the key aspects: what it does, when to use it, and that it provides live data. It doesn't detail the return structure, but the name and description imply a list of slots, and the siblings context (book-meeting) helps the agent infer its purpose. This is sufficient for the tool's simplicity.

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 already provides a comprehensive description of the 'date' parameter, including format and range (today to +60 days, IST). The description adds no additional semantic detail beyond a generic 'for a given date', so a baseline score of 3 is appropriate since schema coverage is 100%.

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: retrieving available consultation slots with the Printing Labs team for a given date. It uses a specific verb ('get') and resource ('meeting slots'), and mentions the team context, distinguishing it from the sibling tool 'book-meeting' which handles booking.

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 'Use before book-meeting', providing a clear directive on when to use this tool relative to its sibling. The context that 'slots reflect the live calendar' reinforces its role as a pre-booking check, making the usage guidance precise 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 of behavioral disclosure. It identifies the tool as returning public details and lists the specific data returned (pricing, GST, stock signal, etc.), which strongly implies a read-only lookup. It does not mention not-found behavior or rate limits, but for a simple retrieval tool this is adequate.

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?

A single dense sentence states the action, input, and complete list of output fields with no filler or redundancy. The key purpose is front-loaded, and every phrase earns its place.

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?

With only one required parameter and no output schema, the description covers the input origin, accepted forms, and the essential return fields. It could add error-handling or not-found behavior, but for this simple tool the description provides enough context for safe selection and invocation.

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 documents the sole parameter with an example, but the description adds important semantics: the slug is a permalink from search-products results and can also be a numeric id. This extra context about parameter provenance and accepted forms goes beyond the bare 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 opens with "Full public details for one product or bundle" — a specific verb-resource-scope statement. It clarifies the input as a permalink slug from search-products results or a numeric id, and enumerates the covered fields, clearly distinguishing this from sibling tools like search-products or calculate-bulk-price.

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 says the slug comes from search-products results, indicating that this tool should be used after a search to retrieve full details for a single product. It does not explicitly list exclusions or alternative conditions, but the intended context is clear.

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?

Annotations are absent, so the description carries full burden. It discloses that it returns 'answers from the published FAQ' and factual policy pages (with 'the page carrying the binding version'), which is useful. However, it doesn't specify whether it's a safe read-only operation or any limits on query scope.

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?

A single focused, front-loaded paragraph with zero filler. Every sentence earns its place: the subject enumeration, the example query usage, and the usage directive.

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 lookup tool with a simple 2-param schema and no output schema, the description is quite complete. It covers what each topic returns and directs the user to authoritative policy pages. It could note which topic is most relevant for typical customers, but this is minor given the clarity.

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 both params described. The description adds value by clarifying what the 'topic' values return (FAQ answers, tool explanations, page lists, policy facts) and gives an example for 'query'. This meaningfully extends beyond the bare enum labels.

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?

Highly specific description: 'Look up how Printing Labs actually works' with enumerated subjects (FAQ, in-house tools, public pages, policies) and named tools (Mockup Studio, Brand Stores, etc.). This sharply distinguishes it from siblings like get-product-details or list-catalogs, which cover different domains.

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 'Use this instead of guessing when a customer asks...' and names the tool list ('tools', 'pages', 'policies'). The description gives clear 'when to use' guidance (customer questions about company operations) and implicitly distinguishes from product/detail tools among siblings.

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 transparency burden. It discloses the return fields but does not state whether the operation is read-only, whether limits or pagination apply, or any other behavioral traits. For a list tool this is somewhat acceptable but still lacks explicit safety cues.

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 sentence that is front-loaded and concise. It lists the key attributes without unnecessary verbosity, earning its place.

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 zero-parameter, simple list tool with no output schema, the description covers the essential return fields. It could mention ordering or pagination, but given the simplicity, it is mostly 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?

There are zero parameters, and schema coverage is at 100%, so the baseline of 4 applies. The description adds no parameter information but none is 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 every published catalogue with specific attributes (title, description, page count, file size, direct PDF URL). It distinguishes from sibling tools by focusing on PDF catalogues rather than product categories or details.

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 usage for accessing all published PDF catalogues, but does not explicitly state when to use it versus alternatives or when not to use it. No exclusions are mentioned.

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 provided, so the description carries the transparency burden. It details the return data (live counts, URLs, preset-bundle count) but does not explicitly confirm whether it is read-only or requires authentication. For a simple listing tool, this is adequate 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 description is a single, dense sentence covering all essential aspects: the resource (categories), the additional data (counts and URLs), and the extra bundle count. No unnecessary 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 zero-parameter, no-output-schema nature, the description is complete in explaining what the tool does and what it returns. It provides enough information for an agent to select and invoke the tool appropriately.

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 description clarifies that it returns 'all' categories, implying no filtering. With no parameters to document, the baseline is high; the description adds no parameter semantics but does not need to.

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 explicitly states what the tool does: lists all Printing Labs catalog categories along with live product counts, canonical category page URLs, and the preset-bundle count. This distinguishes it from sibling tools like list-catalogs (which likely lists catalogs) and search-products (which searches products).

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 usage for retrieving category-level information but does not explicitly state when to use this tool versus alternatives such as list-catalogs. The context is clear, but there is no exclusion or alternative 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 takes on the full burden of transparency. It discloses that the tool creates a real ticket, sends an email/WhatsApp acknowledgment with a reference, alerts the fix-it team, and adds a task with a deadline to a board. This goes beyond a minimal description and informs the agent of concrete side effects.

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, followed by effects and usage direction. Every sentence adds value, with no redundancy or 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?

The description covers key context: what triggers use, what happens after filing (acknowledgment, alert, task), and the exclusion cases. Minor gap: it does not explicitly state the tool's return value (e.g., ticket ID), but the mention of 'reference' implies useful output. Overall, it is complete enough given the absence of an output schema.

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 already provides 100% coverage with descriptions for all 8 parameters, so the baseline is 3. The description adds general context (e.g., complaint categories) but does not provide additional parameter-level meaning beyond what the schema states.

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 purpose: 'File a complaint for a customer who is unhappy with something Printing Labs did', with concrete examples like late/wrong delivery, quality problems, billing errors, and unanswered requests. It explicitly distinguishes this from non-complaint interactions, making it unique among sibling tools.

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?

Usage guidance is explicit: 'Use it the moment somebody complains' and 'do NOT use it for a question, a quote chase or a price negotiation.' This provides clear when-to-use and when-not-to-use directions, helping the agent choose this tool over alternatives like request-quote.

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 reveals that the request is 'real' and 'reaches the team immediately (CRM + email + WhatsApp)', which indicates immediate side effects. It also discloses the consent requirement, adding important behavioral context. It does not mention potential irreversibility, but the immediate notification is sufficiently 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 three sentences long, front-loaded with the primary purpose, and every sentence provides necessary information: what it does, when to use it, and a critical constraint. No fluff or 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?

The tool has 6 parameters and no output schema, so the description should clarify expectations. It explains the action and immediate effect, and includes key usage criteria. It does not explicitly state what the caller will receive as a response, but for a simple request-creation tool, the description is 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?

Schema description coverage is 100%, so the baseline is 3. The tool description adds value beyond the schema by emphasizing that the mobile number must be real and requires explicit consent, which relates directly to the 'mobile' parameter. It also reinforces the importance of 'preferredTime' being an actual date, though that is already detailed 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?

The description clearly states the tool's function: 'Ask the Printing Labs sales team to call the customer back' and 'Creates a real callback request.' It uses a specific verb ('ask'/'creates') and resource (callback request), and distinguishes it from sibling tools like book-meeting or raise-complaint.

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 clear use cases: 'Use when someone wants to speak to a human, or when a voice call needs a follow-up.' It also includes an important exclusion: 'Only submit with the customer's explicit consent and their real number.' While it does not explicitly name alternative tools, the context is clear enough.

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 excels: it discloses real-world consequences (creates a real lead, team receives via CRM+email+WhatsApp, requester gets confirmation) and emphasizes consent. This goes beyond typical mutation descriptions.

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 the action, followed by consequences and a constraint. Every sentence earns its place with zero waste.

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 7-param mutation tool without output schema, the description covers purpose, consequences, and consent requirement. Slightly lacks guidance on when to prefer this over related tools, but overall complete enough for correct invocation.

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 baseline is 3. The description reinforces that contact details must be real and that email receives confirmation, but doesn't add new parameter-specific 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 clearly states the verb and resource: 'Submit a corporate quote request to the Printing Labs sales team.' It also distinguishes from siblings by noting it creates a real lead, unlike search-products or get-product-details.

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 clear context: acts on the user's behalf, requires explicit consent and real contact details. It lacks explicit mention of alternatives (e.g., calculate-bulk-price), but the conditional consent constraint gives practical usage 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 provided, so the description carries the burden of behavioral disclosure. It discloses side effects (Google Calendar event is moved, both parties notified) which is useful, though it does not explicitly state that this is a mutation/destructive action or whether the original booking is destroyed vs. replaced. Still, the disclosed consequences add meaningful value.

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 efficient sentences covering purpose, prerequisites, ordering guidance, and consequences — zero filler. Well structured and front-loaded.

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 3-parameter mutation tool with no annotations and no output schema, the description covers purpose, prerequisites, workflow ordering, and side effects. It does not describe the expected return value, though no output schema exists, leaving a minor gap for what the agent can expect back on success.

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%, with each parameter (date, time, manageToken) already documented in the schema including format examples and provenance. The description reinforces the manageToken source and time format but adds marginal value beyond what the schema already 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?

Clear verb+resource (move meeting) with specific scope ('booked earlier in this conversation') and sibling differentiation: it explicitly references book-meeting and get-meeting-slots, distinguishing this modification/reschedule action from book-meeting and cancel-meeting.

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 prerequisites (manageToken from book-meeting), instructs to check availability with get-meeting-slots first, and provides behavioral consequences (Google Calendar event moved, both sides notified). This gives clear when/how guidance against sibling tools.

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 of behavioral disclosure. It mentions the 'live' nature of the catalog and output as URLs, but does not disclose ordering, default behavior when no filters are applied, or potential rate limits. This leaves some behavioral gaps.

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 primary action, and covers purpose, filters, and output in a compact manner. No fluff or redundant information is present.

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 5 optional parameters and no output schema, the description provides adequate context by explaining the search scope, filters, and return type. It lacks a note on pagination or result ordering, but the schema covers limit details, and the description mentions canonical URLs.

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 already provides 100% coverage with descriptive parameter definitions. The description merely summarizes these filters without adding new semantic meaning beyond the schema. Therefore, it meets the baseline for high schema coverage.

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 searches Printing Labs' live corporate-gifting catalog, listing product types and available filters. It uses a specific verb 'Search' and a resource, and the mention of 'canonical product page URLs' distinguishes it from sibling tools like list-catalogs and get-product-details.

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 clear context for when to use this tool: when a user wants to search the corporate-gifting catalog with filters. It implies a distinct use case from siblings by focusing on search rather than listing catalogs or product details, but does not explicitly state alternatives or exclusions.

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 provided, so the description carries the burden. It discloses that the order must exist and that the email and order ID must match, which hints at read-only behavior and potential validation. However, it does not explicitly state side effects, error behavior on mismatch, or authentication requirements, leaving some gaps.

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, front-loaded with the primary purpose and followed by the key prerequisite. Every word contributes to the tool's understanding, with no fluff or repetition of 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?

For a tool with only two parameters and no output schema, the description is nearly complete. It explains what the tool does (live status timeline) and the input requirements. It does not detail the timeline structure, but for a simple read-only tracker, this is an acceptable level of completeness.

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 already fully describes both parameters (orderId, email) with 100% coverage. The description adds value by explaining the relationship between them—both must match—which is not encoded in the schema. This helps the agent understand the semantic constraint beyond individual parameter definitions.

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 purpose with a specific verb and resource: it provides a 'live status timeline' for an existing order. It naturally distinguishes from siblings, which focus on products, meetings, quotes, and catalogs, none of which overlap with order tracking.

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 clear usage context by specifying the required inputs: order ID and email, and adds the matching constraint. It implies when to use the tool (when you have these identifiers and need status), though it does not explicitly name alternatives or exclusion criteria.

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