In meiner Nähe – local businesses in Germany

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context: the result is probabilistic ('vermutlich', 'möglicherweise') and explicitly 'Nur Indikation, keine Garantie, keine Finanzberatung', which is important for trust and invocation.

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 sentences front-load the action and outcome, then add the necessary caveat. There is no redundant repetition of 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?

The description is sufficient for a read-only hint tool: purpose, matching logic, and confidence level are all present. It does not detail output shape, but it implies the output by saying branches will be marked one way or the other; no output schema exists to require more.

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?

Input schema coverage is 86%, so descriptions of lat/lon/plz/city/radius already exist. The tool description adds semantic value by clarifying that 'your_bank_or_network' is matched against 'gleiches Automatennetz wie die eigene Bank', which explains how the result is determined.

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 ('Markiert') and resource ('Bankfilialen in der Nähe') and clearly defines the two possible classification outcomes. It does not explicitly name or contrast any sibling tool, so it falls short of full differentiation.

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?

No guidance is given about when to choose this tool over find_nearby or the other siblings; the caveat about no guarantee is a disclaimer, not a use case. An agent can infer the use from the name, but the description itself does not state when it is appropriate.

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations: the data source caveat (OpenStreetMap/Overture, not confirmed by the business), the no-login requirement, and the opening-status behavior at a requested time. No contradiction with annotations.

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 compact sentences front-load the core purpose (what, where, sorting, opening status) and then add the data caveat and auth note. Every clause earns its place; nothing is redundant. Slightly long but appropriate given the tool's scope.

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 13-parameter search tool with no output schema, the description covers the core use case well (location types, sorting, opening status, data caveat). However, it does not describe the return format or result fields, which is notable since there is no output schema to fill that gap. It also doesn't mention limit behavior beyond what the schema default suggests.

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 77%, which is high, so the schema already documents most parameters. The description adds marginal value: it links the category examples to the category parameter and the opening-status mention maps to open_at_time. But it doesn't clarify radius semantics, the interaction of plz/lat/lon/city, or the priority of location parameters beyond what the schema provides. 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?

The description uses a specific verb ('Findet'), names the resource ('lokale Betriebe einer Kategorie'), and scopes it precisely to Germany with example categories (Zahnärzte, Banken, Elektriker). It distinguishes itself from siblings: this is the proximity search tool, while get_business and list_categories clearly serve different purposes.

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 use case is clearly implied: find businesses near a PLZ/coordinate/city sorted by distance. However, there is no explicit guidance on when NOT to use this tool or which sibling to prefer (e.g., when a single business detail is needed, use get_business). The context is clear but exclusions/alternatives are 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds what data is returned (address, contact, hours, status), which is useful, but it does not disclose behavior beyond that, such as what happens for invalid IDs or how the optional 'at' timestamp affects the status. Adequate but not rich, given the 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?

A single sentence that front-loads the delivered data and quickly anchors the input to find_nearby. Zero filler; 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 simple read-only detail lookup with 100% schema coverage, rich annotations, and no output schema, the description covers the return payload (address, contact, hours, status) and input provenance. It is slightly thin on the role of the required next to id, but the schema already explains it, so nothing critical 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?

Schema description coverage is 100%, so the schema fully documents all three parameters including the find_nearby provenance of the id and the ISO 8601 format of at. The description repeats the id provenance but adds no new meaning beyond the schema, matching the baseline of 3 for high 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 states a specific verb ('Liefert') and resource (address, contact, opening hours, opening status for a single entry), and ties its input to an ID from find_nearby, which distinguishes it from the sibling search tool. It is clear but does not explicitly contrast it against siblings like atm_fee_hint or sandbox_request_appointment, so it stops one step short of full differentiation.

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 — after find_nearby returns an ID — which gives usable context. However, it does not explicitly state when not to use it, name alternatives, or mention that category is required for invocation. Usage is inferable but not spelled out.

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 destructiveHint=false, covering the safety profile. The description adds valuable context by specifying that the tool returns entry counts (mit Anzahl Einträgen) and is scoped to Germany, which informs the agent about the output. It does not mention pagination or sorting, but for a simple read-only list tool, the added detail is sufficient.

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 main purpose and examples. It contains no fluff or redundant phrasing, and every word 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?

The description conveys the core return behavior (categories with counts) and the optional filter, which is essential given there is no output schema. It could explicitly state the response format (e.g., array of objects) for absolute clarity, but for a simple list tool with a single optional parameter, the information provided is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully documents the single optional 'query' parameter with a clear example ('Suchbegriff, z. B. arzt'). The description only repeats that it is an optional filter, adding no new semantics. With 100% schema coverage, the baseline is 3 and the description does not elevate it.

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 ('Listet'), resource ('Branchen/Kategorien dieses Verzeichnisses'), and geographic scope ('für Deutschland'), with concrete examples. It distinguishes itself from sibling tools like find_nearby or get_business, which target specific locations or business details, so an agent can easily identify when to use this tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when a category list is needed, but it does not explicitly compare to alternatives or state when not to use it. There is no mention of prerequisites or exclusion criteria, so the guidance is only implicit 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?

Beyond annotations (readOnly=false, destructive=false), the description adds important behavioral facts: login is required, the hold lasts 15 minutes, it returns hold_id and expiration time, no price is charged, and nobody is contacted. It does not describe what happens after expiry or how to convert the hold into an appointment, but for a sandbox tool this is sufficient.

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 compact sentence that front-loads the sandbox context and login requirement, then provides the action, output, and side-effect guarantees. Every clause earns its place, and there is no redundant repetition of the title or schema.

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 the essential context an agent needs: sandbox scope, login requirement, fictional business, 15-minute hold, return values, and absence of cost/contact. The only notable gap is the relationship between duration_minutes and the 15-minute hold, and there is no output schema to formalize the returned fields. Still, the description is adequate 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 high (83%), so the input schema already explains most parameters. The description does not add meaningful parameter-level semantics beyond the tool's behavior; it mentions '15 Minuten' as the hold duration, but does not clarify how duration_minutes relates to the hold versus the eventual appointment. Baseline 3 applies because the schema carries most of the parameter burden.

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 action ('merkt einen Slot vor') against a specific resource ('fiktiven Beispiel-Betrieb') and clearly distinguishes the tool from appointment booking by mentioning the 15-minute hold and the returned hold_id and Ablaufzeit. The sandbox context is explicit and 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?

It clearly establishes when this tool is appropriate: in the sandbox, with login, for a fictional business, and for a temporary 15-minute hold with no price and no contact. It does not explicitly name sandbox_request_appointment as the alternative for a firm booking, but the context is clear enough that an agent can infer the distinction.

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 destructiveHint=false, so the safety profile is covered. The description adds valuable context: the data is fictional, it's a sandbox, and the results are only for testing. This is beyond what annotations provide, and it does not contradict them.

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, two sentences, with the sandbox and purpose front-loaded. It avoids redundancy and every phrase contributes to understanding the tool's scope and limitations.

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 is sufficient for an agent to understand when and why to call this tool, given the annotations and schema cover safety and parameters. It does not describe the return format, but since no output schema is provided, the description could have been more explicit about the structure of the returned list. However, for a listing tool, the purpose is clear and the agent can infer the likely shape (list of businesses with slots).

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 documents both parameters with descriptions, including the category slug format and the date format. The description adds no further parameter-specific information, merely referencing the date in the context of the operation. With 100% schema coverage, the description adds minimal semantic value beyond what the schema provides.

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

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

The description clearly states the tool's function: it lists fictional sample businesses with free appointment slots for a given date, explicitly for testing the appointment flow. This distinguishes it from sibling tools like get_business or find_nearby, and the SANDBOX prefix signals its test nature.

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 states the intended use case: 'Zum Testen des Terminflows' (for testing the appointment flow). It also clarifies that real businesses are not bookable, implying this is for sandbox testing. However, it does not explicitly name alternative tools or exclusion conditions, so while the context is clear, it lacks explicit 'when not to use' 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 already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context beyond annotations: login is required and results are restricted to the authenticated user. It does not detail output shape or error behavior, but that is secondary given the read-only annotations.

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 compact sentence with the sandbox context and login requirement front-loaded before the actual behavior. Every element earns its place, and there is no redundancy with the title 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 simple read-only listing tool, the description covers the essential purpose, authentication need, and user scoping. However, with three parameters, no output schema, and weak parameter explanations, an agent gets limited guidance on the optional hold_id and appointment_id fields and the response format. It is adequate but has clear gaps.

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%, with hold_id and appointment_id having no descriptions. The description does not explain what these parameters mean or how they relate to the listed holds and appointments. It only mentions the domain nouns, which is too weak to compensate for the low 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 states a specific action ('zeigt') and resource ('eigene Holds und Sandbox-Termine') and explicitly limits results to the logged-in user. This clearly differentiates it from sibling tools like sandbox_hold_slot and sandbox_request_appointment, which are about creating/reserving rather than listing existing bookings.

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 context: this is a read-only sandbox tool that requires login and returns only the current user's holds and appointments. It does not explicitly name alternatives or state when not to use it, but the scope is clear enough for an agent to select it for listing personal bookings.

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?

Beyond the annotations, the description explains the idempotency behavior (same key returns same appointment), states that login/registration is required, and discloses that no real business is contacted. This is especially valuable for a mutating sandbox 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?

Four short sentences, each carrying distinct information: sandbox scope, approval precondition, idempotency behavior, and isolation from real operations. Important constraints are 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?

The description covers the critical behavioral context: sandbox mode, approval requirement, idempotency, and no real-world effects. It does not describe the return shape, but no output schema exists and the 'same appointment' phrasing gives a usable expectation.

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 75% schema coverage, most parameters are already documented, and the description adds meaning for idempotency_key and reinforces the user_approved precondition. Only hold_id lacks direct documentation, but its role is inferable from the 'converts a hold' context.

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

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

The description names a specific action (converts a hold into a sandbox appointment) and a clear resource, which distinguishes it from siblings like sandbox_hold_slot and sandbox_my_bookings. The title and description together leave no doubt about what the tool does.

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 a concrete precondition: call only after the user has explicitly approved, with user_approved=true. It does not enumerate exclusions or explicitly name alternative tools, but the 'converts a hold' wording and sibling names make the intended sequence clear.

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