Smartwebchat

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

With no annotations, the description carries the full burden and delivers: it records a conversation, consumes the account's message quota, and requires a full-access key because a read-only key returns 401. These are exactly the side effects and auth constraints an agent needs before calling.

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 sentences, front-loaded with the core action, then usage, then behavioral caveats. Every sentence earns its place 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 3-parameter tool with no output schema and no annotations, the description covers purpose, usage, side effects, and auth well. It doesn't describe the shape of the chatbot reply (e.g., whether metadata comes back), a minor gap given no output schema exists.

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 botId, message, and sessionId are already documented in the schema, including the session-continuation semantics. The description adds no parameter-specific detail (like format or bounds) beyond the schema, so the baseline of 3 is appropriate.

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 states a specific verb and resource ('Send a visitor message to a chatbot and get its reply') and clarifies the exact perspective ('exactly as a website visitor would see it'). This distinguishes it from siblings like list_chatbots, create_chatbot, or get_leads, which do not send conversational messages.

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 clear use cases: testing how the chatbot answers and checking knowledge-base coverage. These are concrete triggers for invocation. However, it doesn't state when NOT to use it or name an alternative tool for related tasks, keeping it 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 disclosure burden and does well: it states the call writes to the account, warns to confirm details with the user first, and specifies the required key privilege with the concrete 401 failure for read-only keys. It stops short of describing the response or any rate/latency 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?

Two sentences, front-loaded with purpose then the two operational caveats (user confirmation, key privilege). No filler or repetition of the tool name.

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 9-parameter write tool with no output schema, the description covers purpose, mutation semantics, and auth, and the schema covers every parameter including idempotency. The only gap is that it does not say what the call returns (e.g. the created chatbot's identifier), which an agent creating then configuring a chatbot would benefit from.

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 parameters like personality, industry, and idempotencyKey are already fully documented in the schema itself. The description only echoes a few of the inputs (greeting, personality, FAQ) without adding syntax or constraints beyond it, so baseline 3 applies.

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 verb and resource ('Create a new website chatbot for a business') and enumerates what is created alongside it (greeting, personality, FAQ knowledge), which the bare tool name does not convey. The create semantics are clearly distinguishable from the read-oriented sibling smartwebchat_list_chatbots.

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?

Gives an explicit precondition for calling ('confirm the business details with the user before calling') and an auth gate ('needs a key with full access; a read-only key gets 401 here'). It does not name alternative tools or say when not to use it, but for a creation tool the surrounding conditions are well covered.

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 and does well: it declares 'Read-only', states the return shape (findings with code, severity, remedy), and warns 'Check complete before trusting an empty error list' — a genuine behavioral caveat. It stops short of detailing permissions or failure modes of the tool itself.

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?

Dense but front-loaded: the purpose leads, the checks follow, and the return/caveat sentences close. No filler, though the enumeration is long for a single-sentence run.

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?

No output schema, so it is right that the description explains what returns (findings with code, severity, remedy); no annotations, so the read-only claim and empty-result caveat are necessary. Remaining gap is the absence of any hint about cost or latency of a full diagnostic.

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?

One parameter with 100% schema description coverage, so the schema already documents botId and its source. The description adds no format or ID-syntax detail beyond that, so the baseline 3 applies.

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?

Specific verb-plus-resource framing ('find out why a website chatbot does not answer or does not show up') enumerates the exact conditions checked: access, message limit, credit, LLM provider, origins, knowledge sources, and chat activity. This is clearly distinct from the sibling list/update/create 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?

The trigger condition is stated plainly (chatbot not answering or not appearing) and the schema points to smartwebchat_list_chatbots for the botId. No explicit when-not or alternative-tool routing is given, keeping it 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?

No annotations exist, so the description carries the full burden and does so well: it discloses that no authentication is required, that the verdict is vendor-self-asserted and may be negative, and that the described website is never fetched or verified. It also names the exact return verdicts and the accompanying artifacts, which is unusually candid for a vendor-run 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?

Three front-loaded sentences: verdict, criteria, then behavioral caveats. Dense and mostly waste-free, though the criteria enumeration overlaps with schema fields and could be trimmed. Structure is sound with the negative capability ('never fetches') placed last as a qualifier.

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 an 11-parameter assessment tool with 100% schema coverage and no output schema, the description covers the missing pieces: it explains the return verdict set and their payloads (reasons, limitations, plan, URLs) and the auth and verification posture. Nothing an agent needs to call it correctly is absent.

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 schema already documents all 11 parameters including the locale fallback rule and the agency-plan hint on number_of_client_assistants. The description's criteria list largely restates schema fields, adding no syntax or format detail beyond it, so the baseline 3 applies.

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 verb and resource ('Assess whether Smartwebchat fits a described business') and enumerates the criteria the assessment weighs. This is clearly distinguishable from siblings like smartwebchat_get_quote (pricing) and smartwebchat_service_info (general info) without opening any schema.

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?

Implied usage: it establishes that the assessment is self-asserted, can say no, and needs no authentication, which tells the agent it is a pre-sales/qualification check. However, it never explicitly says when to choose this over get_quote or service_info, and no exclusions are given, so usage is left to inference.

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 full burden. It discloses the default scoping behavior (all chatbots when botId is omitted) and what data the leads contain, but says nothing about result volume, pagination, or any limits — meaningful gaps for a read tool with zero annotation coverage.

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 well-formed sentences with the core purpose front-loaded and no filler. The second sentence largely duplicates the schema's botId description, so it earns its place only marginally.

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 one-parameter read tool with no output schema, the description adequately sketches the returned data (visitor message, contact details, lead types). It does not cover pagination or result-size expectations, which an agent might still need.

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 single optional parameter is already fully documented in the schema. The description restates the same omit-for-all behavior rather than adding format or semantics beyond it, so the baseline 3 applies.

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 verb (Retrieve) plus resource (leads) and even enumerates the lead subtypes it returns — contact requests, appointment bookings, callback requests. It is clearly distinct from the update_lead sibling, but it never names or contrasts with any sibling tool, which the 5-level requires.

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 botId scoping rule ('Without botId, returns the leads of every chatbot in the account') implies when to pass a filter, but there is no guidance on when to reach for this tool versus get_fit/get_quote/update_lead or any stated prerequisites. Usage is inferable rather than explicit.

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 that it works without authentication, that figures are vendor self-asserted from published product facts, that white_label returns 'custom', and that no checkout link is issued (a human completes purchase). These are exactly the behavioral traits an agent needs.

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?

Four dense sentences with no filler, front-loaded with the core action ('Price a Smartwebchat plan'). The caveats (self-asserted figures, no checkout) are appropriately placed but make it slightly heavier than strictly needed.

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 no output schema and no annotations, the description fills the gap by describing the return shape (per-line figures, product facts, quotas), the auth profile, and the purchase caveat. It is close to complete for the task; only pagination/formatting nuance is unaddressed, which is minor for a pricing tool.

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 schema already documents all four parameters including the enum and overage-per-1000 semantics. The description adds light framing (EUR net, included quotas) but no syntax or meaning beyond the schema, so the baseline 3 applies.

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 verb and resource ('Price a Smartwebchat plan with optional add-ons') and clarifies output shape (monthly net EUR, per-line product facts, included quotas). It does not explicitly name or contrast any sibling, but no sibling overlaps in function, so the purpose is unambiguous.

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 is implied by the pricing context and the white-label edge case is called out, but there is no explicit 'use this when' framing or named alternative. An agent can infer intent, yet guidance on when to reach for this versus siblings like get_fit or service_info is absent.

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 full behavioral burden. It discloses the account-scoped, read-only nature implicitly by saying 'List' and names the returned fields, but says nothing about result size limits, pagination, or permissions required; for a trivial zero-param list tool this is adequate but thin.

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 short, front-loaded sentences with zero waste; the resource is named first and the critical ordering instruction follows immediately.

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 no output schema, the description compensates by naming the fields returned (IDs, names, site), which is what an agent needs to chain into other tools. Minor gaps remain around result limits or behavior on an empty account.

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 takes zero parameters, so the baseline is 4 — there is no parameter syntax an agent could get wrong, and the description appropriately describes the output fields instead.

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 verb (List) and resource (website chatbots in this account) and enumerates the returned fields (IDs, names, site), which lets an agent distinguish it from siblings like get_leads or service_info without opening any schema.

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?

Gives explicit sequencing guidance — 'Call this first — every other tool needs a botId from here' — which is effectively a prerequisite/ordering rule across the whole sibling set. It does not name an exclusion or a case where listing is unnecessary, so it falls just short of a full 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 present, the description carries the full behavioral burden and does so well: single-use semantics, a 15-minute expiry, no account/trial creation side effect, no auth requirement, a named failure mode (handoff_disabled) with a status endpoint to check it, and retry/idempotency behavior. This is exactly the disclosure an agent needs before invoking a link-minting 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?

Front-loaded with purpose in the first clause, then layered constraints, permissions, failure mode, and retry guidance in short declarative sentences. No filler; each sentence adds an operational fact the agent must act on.

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 5-param, no-required-param tool with no output schema, the description covers behavior, error modes, auth, and retry semantics thoroughly. The one gap is the return shape — it never says what the response contains (e.g. the link itself) beyond hinting that a disabled operator 'answers handoff_disabled', which an agent would need since no output schema exists.

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 every parameter (locale, industry_key, idempotencyKey, demo_session_id, recommended_plan) is already documented in the schema, including fallback behavior and the enum source. The description mostly restates the carried fields and the idempotencyKey retry rule rather than adding syntax or format meaning, so the baseline 3 applies.

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 verb+resource ('Prepare a single-use sign-up link for a person') and immediately scopes what it carries (language, industry, recommended plan). It also carves out what it is NOT ('does not create an account or a trial'), which no sibling tool does, so an agent can separate it from create_chatbot or get_fit without opening 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?

Gives clear operating context: works without authentication, the operator can disable it (then answers handoff_disabled), and /agents/start shows current status. It also implies when not to rely on it ('does not create an account or a trial'). It stops short of naming an alternative tool or an explicit when-to-prefer-this condition, so it is not a full 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 burden and discloses a key behavioral trait: it works without authentication. The verb 'Get factual information' implies a read-only, side-effect-free operation, but it does not explicitly state safety, rate limits, or response format.

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 core purpose and followed by the usage context. Every phrase adds value and there is 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?

Given zero parameters, no output schema, and no annotations, the description is complete: it explains what information the tool returns, that it needs no authentication, and when to use it. Nothing essential for an agent to call it correctly 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?

The tool takes zero parameters, so the baseline score is 4. The description appropriately adds no parameter details because there is nothing to parameterize.

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 verb and resource: 'Get factual information about the Smartwebchat service itself.' It then enumerates the exact topics covered (plans, prices, languages, data processing, API keys), which clearly distinguishes it from all action-oriented 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?

It explicitly says when to use it: 'use it to answer questions about Smartwebchat before the user has an account,' and notes it works without authentication. This implies when not to use it (after account creation, use other tools) but does not name any alternative tools explicitly.

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 useful work: it discloses that this writes to the account and that a read-only key returns 401, which is exactly the kind of auth/mutation context an agent needs. It omits any note on idempotency, allowed status transitions, or failure behavior for invalid leadId/botId.

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 tight sentences with no filler; the core action is front-loaded and the auth caveat follows immediately. Every clause 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?

For a 3-param mutation tool with no output schema, the description covers purpose, mutation semantics, and auth requirements. A brief note on what a successful update returns or on permitted status transitions would close the remaining gap.

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% and the enum already lists 'new', 'contacted', 'closed', so the schema fully documents all three required parameters. The description's status examples add nothing beyond the enum, so the baseline 3 applies.

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 verb and resource ('Change the status of a captured lead') with concrete examples of the target states. An agent can immediately distinguish this mutation tool from the sibling read tool smartwebchat_get_leads without opening either schema.

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?

Gives clear usage context ('for example to mark it as contacted or closed'), so the agent knows the intended scenario. It does not name an alternative tool or state when not to use it, which keeps it 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.