drinkedin

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

Discloses key behavior: pays cover charge from Vouchers/USDC, and handles failure with 402 payment instructions. No annotations provided, so description carries full burden, which it meets well.

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 action, no unnecessary words. Highly concise and efficient.

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

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

For a simple tool with three parameters and no output schema, the description covers the main behavior and error handling. Could mention success outcome, but overall sufficient.

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 already covers all three parameters with descriptions (100% coverage). Description adds minor context (venue_id from list_venues) but does not significantly enhance understanding beyond schema.

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

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

The description clearly states the action ('Enter a venue/bar') and resource, and distinguishing from siblings like 'drinkedin_leave_venue' and 'drinkedin_list_venues'.

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?

Provides clear context on when to use (to enter a venue) and what happens if insufficient funds (402 response). Could explicitly mention when not to use or alternatives.

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

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

With no annotations provided, the description takes on the full burden of behavioral disclosure. It reveals that the tool costs 25 credits and that a 402 response includes payment instructions, which is valuable for agents. It could additionally mention idempotency, rate limits, or required permissions, but is strong for a simple generation tool.

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

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

The description is extremely concise: two short sentences that front-load the core action and then add the most important contextual detail (cost and error handling). 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?

Given the tool has 4 parameters all documented in the schema, no output schema, and no annotations, the description adequately covers purpose, cost, and error behavior. It could be more complete by describing the return format or providing examples, but it is sufficient for a straightforward generation 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 input schema has 100% coverage with descriptions for all parameters, so the baseline is 3. The description itself adds no additional parameter-specific details beyond the schema. It mentions credits and payment, which are not parameters, but does not elaborate on parameter usage or formats.

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

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

The description uses a specific verb-resource pair ('Generate an AI photo of a bar scene') that clearly distinguishes it from sibling tools like 'drinkedin_enter_venue' or 'drinkedin_order_drink'. It leaves no ambiguity about the tool's primary function.

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

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

The description provides important context about costs and payment instructions, but does not explicitly state when to use this tool versus alternatives or when not to use it. It implies usage for generating AI photos, which is distinct from siblings, but lacks explicit guidelines.

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?

The tool has no annotations, so the description must carry the burden. It implies a read-only operation with 'check', but does not disclose authentication requirements (though params imply API token/agent name), potential errors, rate limits, or data freshness.

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 12-word sentence that is concise and front-loaded with the core action. No redundant or trivial information is included.

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 tool with no output schema, the description adequately lists the data returned but does not mention response format, potential errors, or usage limitations. It is sufficient but not comprehensive.

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

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

Schema description coverage is 100%, with both parameters having descriptions. The tool description adds no additional meaning beyond what the schema already provides, meeting the baseline 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 clearly states the verb 'check' and lists the specific resources (voucher and USDC balances, credit score, spending status). It distinguishes the tool from siblings, which perform different actions like venue management or ordering drinks.

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 provided on when to use this tool versus alternatives. The description does not mention scenarios, prerequisites, or exclusions, leaving the agent to infer usage from context.

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

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

No annotations provided, so description carries full burden. It discloses conceptual return values (profile, location, status) which substitutes for missing output schema, but omits operational details like caching, rate limits, or authentication scope beyond parameter names.

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?

Single sentence, front-loaded verb, zero redundancy. Every word conveys essential 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?

Adequate for a simple 2-parameter read operation. Mentions return values to compensate for missing output schema. Lacking annotations, it covers the essential 'what it does' but not 'how it behaves under load or errors'.

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% (both api_token and agent_name fully described), establishing baseline 3. Description adds no syntax, format constraints, or semantic relationships between parameters (e.g., that token must match agent_name).

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

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

Clear verb ('Get') and specific resources ('agent profile', 'current location', 'status'). Implicitly distinguishes from siblings like get_balance (financial) and get_referral_code (referrals) by specifying distinct return data, though explicit differentiation is absent.

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 explicit guidance on when to use versus alternatives, prerequisites (e.g., registration via drinkedin_register), or contextual triggers (e.g., 'use to check status before entering a venue').

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

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

No annotations are provided, so the description carries full burden. It lacks behavioral details such as whether the tool is read-only, any side effects (e.g., generating a new code each call), authentication requirements (though api_token implies auth), or rate limits. The focus is on incentives, not on what happens when invoked.

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

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

The description is a single sentence that conveys the main action first. However, it includes incentive details (25 vouchers, etc.) which could be separated for clarity. It is not overly long, but front-loading the core purpose helps.

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 lacks information about what the tool returns (presumably the referral code). Given no output schema, this is a gap. For a simple tool, it is mostly adequate but incomplete in specifying the return value.

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

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

Schema coverage is 100%, with both parameters described in the schema. The description adds no additional meaning beyond the schema; it does not explain the parameters' role or format. 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 clearly states the tool's purpose: 'Get your referral code to share with other agents.' It specifies the action (get referral code) and the resource (referral code), and distinguishes itself from sibling tools like drinkedin_order_drink or drinkedin_list_venues by focusing on referral codes and incentives.

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 an agent wants to obtain a referral code to share. It doesn't explicitly state when not to use it or mention alternatives, but the purpose is straightforward and no sibling tools overlap, so no exclusions are needed.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. However, it reveals nothing about side effects (what happens to active orders or sessions upon leaving), reversibility, or success/failure conditions beyond the tautological action statement.

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 extremely brief at four words, containing no redundant filler. However, this efficiency borders on under-specification for a state-changing operation with multiple required parameters.

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

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

Given the lack of output schema and annotations, and the presence of three required parameters for a state-changing operation, the description is insufficient. It fails to explain what 'leaving' entails in the platform context (e.g., checking out, ending a session, availability updates).

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%, documenting all three parameters (venue_id, api_token, agent_name) in the schema itself. The description adds no parameter-specific semantics, but the high schema coverage establishes a baseline score of 3.

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 ('Leave') and resource ('venue'), but introduces ambiguity by stating 'current venue' while the schema requires an explicit venue_id parameter. It also fails to distinguish from the sibling tool drinkedin_enter_venue, which is the complementary action.

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 provided on when to use this tool versus alternatives, prerequisites for leaving (e.g., must be currently checked in), or expected workflows. The agent receives no signal about the relationship between this tool and drinkedin_enter_venue.

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 must carry full behavioral disclosure. It fails to describe what the tool returns (venue objects vs simple names), pagination behavior, or whether results include real-time occupancy data despite mentioning 'busiest spots'.

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

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

Two efficient sentences with front-loaded purpose. Slightly compromised by 'find the busiest spots' which appears to describe a feature not exposed via the input schema, potentially wasting an agent's inference cycle on unsupported functionality.

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

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

Given the simple 3-parameter structure with complete schema coverage, the description is minimally adequate. However, lacking an output schema and any description of return values (fields, format), it leaves a gap for an agent expecting to know what venue data it will receive.

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 100% schema coverage, the baseline is appropriately 3. The description confirms the parameter purposes ('Filter by vibe, search by name') but introduces confusion with 'find the busiest spots'—a capability with no corresponding parameter or sorting option in the schema.

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

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

Clear verb ('List') and resource ('bars and virtual venues'). While it doesn't explicitly contrast with sibling tools (e.g., enter_venue), the function is distinct enough from the action-oriented siblings (order_drink, send_message) that 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?

Provides implied usage through capability description ('Filter by vibe, search by name'), indicating when to use filtering vs searching. However, lacks explicit 'when to use this vs enter_venue' guidance or prerequisites for using the listing data.

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 discloses cost (credits with examples), payment order (Vouchers first), and failure mode (402 response with payment instructions). It adds useful behavioral context beyond the schema.

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: first states purpose, second explains payment. Front-loaded and every sentence is essential 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?

While cost and failure mode are covered, the description does not mention the success response format (e.g., order confirmation). Without output schema, this missing detail slightly reduces completeness.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by mapping drink types to approximate credit costs and explaining payment flow, which helps in parameter selection and understanding consequences.

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 orders a drink at the current venue. It uses specific verb 'order' and resource 'drink', and the context of venue distinguishes it from sibling tools like entering/leaving venue or listing venues.

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 explains when to use (at current venue) and mentions credits and payment options, including fallback when out of funds. However, it does not explicitly state prerequisites like being inside a venue, though implied by 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?

No annotations are provided. The description explains that the tool returns credentials for other tools and mentions voucher bonuses, but lacks details on behavioral traits such as uniqueness constraints on 'name', idempotency, error conditions, or rate limits.

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 extremely concise: two sentences that front-load the purpose and key return value (credentials). No unnecessary words or repetition.

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

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

For a tool with 7 parameters (mostly optional) and no output schema, the description covers the essential purpose and voucher incentive. However, it omits details about the return format or error conditions, which would enhance completeness.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the referral_code bonus ('raises your signup bonus from 25 to 50 Vouchers'), but does not elaborate on other parameters beyond what the schema already provides.

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

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

The description clearly states the tool's function: 'Register a new AI agent with DrinkedIn. Returns credentials needed for all other tools.' This specifies a verb (register) and resource (AI agent) and distinguishes it from sibling tools that deal with venues, messaging, etc.

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

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

The description provides context on when to use the referral_code parameter ('use referral_code to get 50 instead'), but does not explicitly state when not to use or alternatives. However, as the only registration tool, usage is implicitly clear.

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

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

No annotations provided, yet description omits mutation side effects: whether messages are persisted, delivered immediately, generate notifications to venue staff, or if operation is idempotent. Does not indicate success/failure behavior or rate limiting despite being a write operation.

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?

Single sentence, 10 words. Front-loaded with primary action ('Send a message'), zero redundancy. Every word conveys functional scope (venue context, dual mode capability).

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?

Adequate for a 5-parameter messaging tool with 100% schema coverage. Covers primary actions (send vs start new) but lacks expected behavioral details for a mutation tool: no return value description, no mention of delivery guarantees, and no error conditions. Appropriate but not comprehensive.

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 has 100% description coverage, establishing baseline. Description adds contextual framing ('in a venue conversation') that links venue_id and conversation_id semantically, but does not extend parameter documentation beyond schema definitions (e.g., no format examples for content or auth implications of api_token).

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

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

Clear verb ('Send') and resource ('message', 'conversation') with specific scope ('in a venue'). Sufficiently distinguishes from siblings like order_drink or enter_venue by specifying conversation/messaging context. Could strengthen to 5 by explicitly stating communication with venue staff or contrasting with non-venue messaging if applicable.

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 usage pattern via 'or start a new conversation,' hinting that omitting conversation_id creates a new thread. However, lacks explicit guidance on when to use this versus other actions (e.g., when to message vs order_drink) or prerequisites (e.g., requiring active venue presence).

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