WirStartenKI

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so safety is covered structurally. The description adds the substantive behavioral context: the scope of what is returned (identity, consulting coverage, call process). It does not mention rate limits or auth, but none are relevant for a static informational resource.

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 front-loaded sentence that lists the three content buckets with no filler. It is appropriately sized for a zero-argument informational tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no input schema, no output schema, and a read-only static resource, the description covers everything an agent needs to decide to call it. A brief note on when to prefer it over the booking siblings would make it 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 tool takes zero parameters, so per the rubric the baseline is 4. The description makes no parameter claims that could mislead, and there is nothing for it to clarify.

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

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

The description names a concrete information resource (Felix Schmidt and WirStartenKI) and enumerates its content: who they are, what the consulting covers, and how a first call works. That distinguishes it from the transactional siblings (book_call, get_open_slots), though it never names them 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?

Usage is only implied: an agent can infer this is the discovery/background tool to consult before booking. There is no explicit 'use this when...' or 'do not use for...' statement tying it to book_call or get_open_slots.

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 safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true, idempotentHint=false). The description adds non-obvious behavior: the 15-minute duration, that exactly one call is booked, and that Google emails the invite with a Meet link to the given address — an external side effect the annotations don't convey.

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

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

Three short sentences, front-loaded with the core action, then the precondition, then the side effect. No filler and nothing repeated from the schema or annotations.

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 booking tool with no output schema, the description covers the action, the precondition, and the resulting external email. It omits failure behavior (e.g., slot already taken) and the meaning of the language parameter, which are minor given 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?

Schema description coverage is only 33% (only topic and slot_start documented), so the description must compensate. It clarifies that slot_start must be an open slot and ties the email parameter to the invite delivery, but company, language, and name remain undocumented in both places.

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 concrete verb and resource with scope: 'Books one 15-minute video call in an open slot.' This clearly separates it from get_open_slots (read-only slot listing) and about_wirstartenki, though it never names those siblings 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 real precondition for invocation: 'Only after the person confirmed the time and their e-mail.' Combined with 'in an open slot' it implies the get_open_slots prerequisite, but it never explicitly routes the agent to that sibling or states 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?

Annotations already establish this is a safe, idempotent, non-destructive, closed-world read. The description adds genuinely new behavior: the data is live from the calendar, each slot is 15 minutes, and results are emitted both in the requested IANA zone and as UTC ISO strings. It omits volume/pagination details, which keeps it out of the top band.

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 load-bearing: the first defines the output, the second defines the format and the consuming tool. Scoping and return format are front-loaded with 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 correctly supplies the return format (local + UTC ISO) and the purpose/consumer linkage. The only omission is any indication of how days_ahead bounds the result set.

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 50%: timezone is documented in the schema, but days_ahead has no description at either layer. The description usefully clarifies that timezone governs how returned times are expressed, but adds nothing about the days_ahead look-ahead window, so it only partially compensates for the gap.

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

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

The description names a concrete resource (free 15-minute video-call slots) and its source (Felix's live calendar), which is unmistakably distinct from the sibling book_call that consumes the result. An agent can tell what this returns without opening the 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?

It links itself to book_call, making the discovery-then-booking workflow clear, but never states an explicit when-to-use condition or any exclusions (e.g., fallback if no slots exist). Clear context, no negative 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?

Adds real context beyond the annotations: the analysis is deterministic, free and needs no sign-in, and supplying the optional e-mail persists the result and returns a personal report link — a side effect not derivable from readOnlyHint=false alone. It does not mention latency or limits, which keeps it below a 5.

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 value proposition, then the input inventory, then the return payload in three tight sentences. The 15-topic enumeration partly duplicates the schema, but it is compact and serves as a checklist for elicitation.

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 19-parameter tool with no output schema, the description carries its weight by describing the return shape (three 0-100 scores with readings, three ranked levers, an effect range, a short assessment). Only the optional language/output-locale and name fields are left to the 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?

With 89% schema coverage the schema already defines every enum, but the description reframes the 15 required fields as questions to be put to the user (role, size, cost block, time sink, ownership, mandate, timing), which tells an agent how to elicit them rather than how to format an API payload. It does not explain the optional company, first_name or language fields.

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

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

Names a specific resource and outcome: an AI impact/maturity diagnostic that takes 15 multiple-choice answers and returns maturity, impact and momentum scores plus ranked levers. The three siblings (about_wirstartenki, book_call, get_open_slots) are clearly unrelated, so no disambiguation is needed.

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 'Where AI pays off first in a company', but there is no explicit when-to-use/when-not or guidance on how to run the assessment conversationally with a user. Invocation conditions that are stated (deterministic, free, no sign-in) are about friction, not about tool selection.

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