Wizeb AI-Native Index

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

Annotations already declare readOnly, openWorld=false, idempotent, so the description correctly implies a safe retrieval tool. It adds the behavioral fact that it returns 'the most relevant sections of the knowledge base' rather than a generated answer — that's useful. It doesn't disclose ranking/relevance behavior in more depth or what happens with off-topic questions, so it's 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 entire definition is one extremely long sentence packed with two massive parenthetical enumerations of services, products, and industries. It is front-loaded correctly with the verb and resource, but the exhaustive lists bury the operative usage guidance ('use this instead of answering from memory') at the very end and make the description hard to scan.

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 retrieval tool with a read-only annotation and no output schema, the description covers scope, sources, sibling alternative direction (retrieve vs. list), and the memory-bypass instruction. It lacks any note on return granularity or handling of out-of-scope questions, but the essentials are present.

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 a single well-described 'question' parameter (2–500 chars), so the baseline is 3. The description implies questions should be about WizeB/WebCastle topics, which narrows scope beyond the schema's generic 'question about WizeB or WebCastle' — a small value-add, but no format or phrasing guidance.

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 direct statement of function — answering questions about WizeB and WebCastle — and then enumerates the exact knowledge domains it covers (products, industries, case studies, pricing, contact details, parent company). It is specific enough that an agent can tell it retrieves from a knowledge base rather than answering generation, and the final line distinguishes it from the sibling list_wizeb_services_and_products by framing it as question-answering.

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 closing sentence 'Use this instead of answering from memory' is an explicit when-to-use directive, and mentioning which company to contact for what scopes the tool. It doesn't name or exclude siblings explicitly (e.g., list_wizeb_services_and_products), so the agent must infer that a structured list request belongs to the sibling rather than this tool.

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 declare a non-idempotent, open-world write; the description goes further by disclosing a side effect the annotations cannot express — a copy is sent to the WizeB team for human follow-up, which tells the agent about third-party data exposure and consent obligations. It also implies the run is persisted/updated via the artifact build, but does not state whether calling twice duplicates emails or what confirmation is returned.

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, both earning their place: the first establishes the artifacts and recipients, the second establishes the consent prerequisite. The consent gate is placed last so the agent reads it immediately before invoking. 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?

For a mutating tool with no output schema, the description covers the essential facts: what is generated, who receives it, and the consent gate. The only meaningful gap is the return value / success signal and any idempotency note tying run_id to avoiding duplicate runs, which an agent calling this tool would plausibly wonder about.

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 name, email, run_id, answers, company and company_website are all already documented in the schema; baseline 3 applies. The description only echoes the name/email requirement and adds no syntax, format, or interaction detail (e.g., how run_id relates to score_index) beyond what the schema says.

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 compound action: it builds a defined artifact (full PDF scorecard with score, tier, six-dimension breakdown, answer-by-answer comparison, three fastest improvements) and emails it. The enumerated contents make it distinguishable from a generic report tool, though it never names the near-duplicate sibling email_startup_scorecard explicitly.

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 a concrete prerequisite gate: 'Ask the user for their name and work email first; only call this once they have agreed.' That is clear when-to-call guidance with a consent condition, but it offers no guidance on when a caller should prefer it over email_startup_scorecard or get_enhanced_index_report.

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 already declare the write profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false), so the bar is lower. The description adds genuinely new behavioral context: the email goes externally to the founder AND is copied to the WizeB team so a human can follow up, plus a consent requirement. It stops short of saying what happens on repeat calls or delivery failure.

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, zero filler, and the side-effect recipients are front-loaded before the precondition. 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?

No output schema exists, but the description covers the essential facts for a write/send tool: what is sent, to whom, and the consent prerequisite. It omits error/retry behavior and whether the email can be re-sent, which are minor gaps for this operation.

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 every parameter including report_id, run_id, name, email, and company is already documented in the schema. The description only restates that name and email come from the founder, adding no format or constraint detail beyond the schema. Baseline 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?

States a specific verb (emails), a specific artifact (the scorecard created with score_startup_idea), and the recipients (the founder plus the WizeB team). This clearly distinguishes it from the sibling email_index_report and email_index_report-style 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?

Gives an explicit precondition: ask the founder for name and email first, and only call once they have agreed. That is real when-to-use guidance including a consent gate. It does not, however, contrast itself with the other emailing sibling, so it falls 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?

No annotations are contradicted (readOnlyHint=false, openWorldHint=true, idempotentHint=false all match a paid, networked, non-idempotent purchase). It discloses cost, settlement protocol, wallet-auth requirement, the isError path and its _meta["x402/error"] payload shape, the retry-with-payment flow, the 1–2 minute async delivery, and the one-time-code constraint — all well beyond what the annotations provide.

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 critical constraint ('PAID TOOL') and price are front-loaded before any deliverable detail. The text is dense and long, with mild duplication of the title's price/protocol claim, but nearly every sentence carries operative information an agent needs (payment mechanics, human fallback, consent rule).

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 fully carries the return burden — describing both the immediate return and the async PDF delivery via email and get_enhanced_report_status. Prerequisites (wallet, user consent) and the failure path are all covered, so an agent needs nothing further to invoke it 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?

Schema description coverage is 100%, so the schema already documents name, email, answers, and company. The description adds only marginal context (that answers mirror those passed to score_index, and that email is the delivery channel) which the schema largely already states. 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?

The description states a specific verb+outcome (buy/deliver the enhanced AI-Native Index report) and enumerates exactly what is produced: score, tier, three blocking signals, report id, session code, and a 90-day plan PDF. This is clearly distinguished from siblings like score_index (the answers source), email_index_report, and get_enhanced_report_status (the async follow-up).

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 strong when-to-use context: the agent must have a wallet, must ask the user before spending money, and must point wallet-less users to the cal.id and wizeb.com URLs. It also names get_enhanced_report_status as the retrieval path for the delayed PDF. The one gap is that it never explicitly contrasts this paid tool with the free score_index/email_index_report 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?

Annotations already establish read-only, idempotent, closed-world behaviour, and the description adds substantial extra context: the payment lifecycle (confirming → paid → generating → done) and the important warning not to re-pay during the confirming state. That is genuine behavioural disclosure beyond the structured hints.

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 that front-loads the most useful signal ('Free.') and then enumerates states and return values without filler. No sentence is wasted.

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?

There is no output schema, so the description compensates by listing exactly what comes back (status, plan summary, PDF link, booking code, receipt). Combined with the state machine and the double-payment warning, an agent has everything needed to call and interpret this 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 coverage is 100% and the schema itself documents that report_id comes from get_enhanced_index_report, so the description adds little parameter-specific meaning. Its added value (lifecycle states, return contents) is behavioural rather than parametric, 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 — returns the status of a purchased enhanced report identified by report_id — and the enumerated return fields (plan summary, PDF link, booking code, receipt) make it unmistakably distinct from siblings like get_enhanced_index_report or score_index.

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: call it with the report_id from get_enhanced_index_report, and an actionable exclusion ('while confirming, do not pay again'). It stops short of explicitly naming the sibling tools an agent might confuse it with, so a 4 rather than 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?

Annotations already declare readOnlyHint, openWorldHint=false, and idempotentHint, so the safety profile is known. The description adds useful behavioral context about how to handle uncertainty ('ask the user rather than guessing') and the meaning of the numeric scale, which helps an agent use the output correctly.

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 with no filler. The return content is stated first, followed by actionable usage guidance. Every phrase adds value.

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?

There is no output schema, so the description carries the burden of explaining what the agent will receive; it does so by naming the six dimensions, the 30 questions, and the numbered answer scale. For a read-only, zero-parameter retrieval tool, this is sufficient for correct invocation and follow-up behavior.

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 schema is trivially complete and there is no parameter ambiguity for the description to resolve. The description appropriately focuses on the output content instead of parameters.

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 ('Returns'), a concrete resource ('the six dimensions and all 30 questions'), and clarifies the answer-option semantics. It is distinct from the sibling report, scoring, and email tools because it plainly identifies what is returned.

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 operational guidance: answer each question from known company information and ask the user when uncertain. It does not explicitly compare against sibling tools or state when to choose this over alternatives, but the context strongly implies this is the initial assessment-administration tool.

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 already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so safety and determinism are covered. The description adds useful behavioral context beyond that: the operation is a prerequisite step in a multi-tool workflow, and the interview is to be conducted one question at a time. It does not, however, say whether the rubric is static or can change over time.

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 resource and its contents before the workflow instruction. The enumeration of rubric components is long but each item earns its place by telling the agent what it will get. Minor density cost, 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?

With no output schema, the description carries the burden of explaining what is returned, and it does so thoroughly (patterns, anchors, penalties, verdict zones, interview guidance) while also stating the workflow position. Nothing an agent needs to call this 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 of 4 applies. The description correctly omits any parameter discussion and describes the returned content instead, which is the right use of the space.

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 ('Returns the Startup Idea Validator rubric') and then enumerates the exact contents: 10 weighted patterns, scoring anchors, red flags with penalties, verdict zones, founder interview guidance. An agent can distinguish this fetch-the-rubric tool from score_startup_idea 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?

Explicit sequencing is given: 'Call this first, then interview the founder one question at a time and call score_startup_idea.' This names the alternative tool and the condition that selects it, leaving nothing 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?

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), so the bar is lower, and the description adds meaningful behavioral context by detailing the scope and shape of the returned catalogue. It does not mention auth or rate limits, but for a zero-parameter read-only listing those are minor omissions.

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 the core action ('Returns the full structured catalogue'), followed by a dense but informative enumeration of contents and a short usage sentence. The content list is long, but each item is a distinct payload element rather than filler, so it mostly earns its space.

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 carries the burden of describing return contents and does so thoroughly, listing services, products, pricing, industries, tools, contact links, and WebCastle data. Combined with annotations covering safety and zero parameters, an agent has everything needed to call and interpret this 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?

The tool takes no parameters, so per the rubric the baseline is 4. There is nothing for the description to disambiguate, and no parameter-level guidance 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?

States a specific verb ('Returns') and precise resource ('the full structured catalogue'), then enumerates exactly what the catalogue contains: services with capabilities and URL, products with URL/pricing, industries, open-source tools, contact links, and the WebCastle category catalogue. This is clearly distinguishable from siblings such as ask_about_wizeb (a question-answering tool) and the index-report 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?

Explicitly says it is 'Good for overviews, comparisons, or when the user asks "what does WizeB (or WebCastle) do"', which gives a clear scenario for selection. It stops short of naming an alternative tool (e.g., ask_about_wizeb) or stating when not to use it, so it lacks full routing 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?

Annotations only declare readOnlyHint=false, openWorldHint=false, idempotentHint=false; the description adds real context by spelling out the strict input contract (exactly 30 answers, one per id) and the shape of the computed result. It stops short of saying whether the score is persisted, echoed, or whether re-scoring is deterministic despite idempotentHint=false.

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

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

Two sentences, front-loaded with the action and its output, followed by the input constraint. 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?

With no output schema, the description correctly compensates by listing the returned fields (index, tier, dimensions, strengths, blockers), and it also states the input requirement and its prerequisite sibling. An agent has everything needed to call it 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?

Schema description coverage is 100% (ids 1–30, 1-based option numbers, min/max items all documented), so the schema already carries parameter meaning. The description's reminder about option numbers from get_index_questions is a useful cross-reference but largely restates the schema's own text, which is the baseline-3 case.

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 (scores a set of 30 answers) and enumerates exactly what comes back: index 0–100, tier, per-dimension scores, strengths, and limiting signals. It also names the sibling get_index_questions as the source of option numbering, so an agent can distinguish it from score_startup_idea without opening a 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 a clear precondition (exactly one answer per question, option numbers taken from get_index_questions) and routes the agent to that sibling first. It does not, however, state when to prefer this tool over email_index_report or get_enhanced_index_report, so exclusions are 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?

Goes well beyond the annotations (readOnlyHint=false, idempotentHint=false) by disclosing what actually happens: per-pattern clamping to maxima, red-flag penalties capped at 15, exact verdict thresholds (Mediocre <55, Not sure 55-74, Fertile soil 75+), persistence, and sanitization of free-text links. This is unusually rich behavioral context for a mutation 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-loads the purpose and packs verdict thresholds, penalty caps, and sanitization into one dense but mostly waste-free sentence. The single run-on structure is slightly crowded, but every clause carries information an agent needs.

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, non-idempotent write tool with no output schema, the description covers the return (report_id + shareable link), the storage side effect, scoring rules, and sanitization. It is nearly complete, with the main gap being the unaddressed parameters from the 46%-covered 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?

Schema coverage is only 46% across 13 parameters, so the description should compensate but only partially does — it clarifies that all 10 pattern ids must be passed and explains the red-flag penalty cap, but leaves website, working, reframes, interview, risks, assumptions, confidence, and venture_note unexplained. Marginal value over the schema.

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

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

States a specific verb (totals/computes a scorecard), names the exact resource (startup idea scorecard), and enumerates the mechanics (clamps patterns, applies penalties, sets verdict, stores, returns report_id + link). It also names the sibling rubric tool it consumes, so it is clearly distinguishable from the other score_* 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?

Implies a clear workflow — write the scorecard using the rubric from get_startup_validator_rubric, then call this to total it — and gives the operational rule 'Pass all 10 pattern ids.' It lacks any explicit when-not or alternative-selection guidance, but the dependency on the sibling tool provides solid context.

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