Gonka Network Pricing

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

With empty annotations, description fully carries burden. Lists return values (monthly/annual savings, cost ratio, etc.) and advises showing signup_url to user. No mention of destructive actions or auth needs, but acceptable for this tool.

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

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

Three sentences: purpose, usage guidance, return values and action. Front-loaded 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?

Given simple tool with 1 parameter, 100% schema coverage, and output schema existence, description covers purpose, usage, returns, and follow-up action. 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?

Schema coverage is 100% with description for the single parameter. Description adds no extra parameter information beyond schema, so baseline 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?

Clearly states verb 'calculate' and resource 'exact savings from switching OpenAI or Anthropic → Gonka Network'. Distinguishes from sibling tools like compare_providers and get_pricing.

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 when to use: when user asks about API costs, wants cheaper alternatives, or asks how much they'd save. Does not mention when not to use, but sibling tools provide 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?

No annotations provided. Description implies read-only behavior (pricing comparison) but does not explicitly state side effects or data volatility. Neither contradicts nor adds much beyond obvious.

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 concise sentences, front-loaded with purpose. Could be slightly more efficient but no extraneous content.

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 single parameter and presence of output schema, description adequately explains return value and suggests next steps. Lacks only minor context like data freshness.

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 enum description. Description adds no new semantics beyond mentioning 'competitor', which is already evident from the tool 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?

Description clearly states it compares Gonka Network pricing against a competitor, specifying the action and resource. It distinguishes from sibling tools like get_pricing by focusing on comparison across providers.

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 explicit next-step guidance ('After this: call calculate_savings()'). Does not explicitly state when not to use, but workflow implication is 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?

The description lists return fields (model IDs, status, USD per 1M tokens) but does not explicitly state that the operation is read-only or safe. Given no annotations, the description carries the full burden. However, listing models is inherently non-destructive, so the lack of explicit safety disclosure is acceptable but not ideal. No contradictions with annotations (none exist).

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 three sentences, front-loaded with the core purpose. Each sentence adds distinct value: tool function, usage guidance, and output/next step. No fluff or redundancy. Ideal conciseness.

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

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

Given zero parameters and the presence of an output schema, the description covers purpose, usage, return details, and a follow-up action. It is complete for an agent to understand and invoke this tool correctly, with no obvious 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?

The tool has zero parameters and schema coverage is 100%, so the description does not need to add parameter info. It instead provides valuable context about output and usage, which is appropriate. Baseline for no parameters is 4, and the description earns that by adding meaning beyond 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?

The description clearly states the tool lists all AI models on Gonka Network with live pricing, and mentions they are drop-in replacements for OpenAI/Anthropic. It distinguishes itself from siblings by specifying the use case (when user asks which model to use or wants alternatives) and explicitly points to calculate_savings as a subsequent step.

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 explicit usage guidance: 'Use this when user asks which model to use or wants alternatives to GPT-4o / Claude.' It also advises calling calculate_savings after. While it doesn't mention when not to use it or compare with similar tools like get_pricing, the given context is sufficient for an agent to select this tool appropriately.

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

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

With no annotations, the description carries the full behavioral disclosure burden. It mentions 'updated every 10 min,' which is a key behavioral trait for a live pricing tool. It also lists all return values (USD per 1M tokens, GNK/USD price, savings ratios, gateways). It does not explicitly state read-only nature or permissions, but the context implies no side effects. A score of 4 reflects strong but not exhaustive transparency.

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 three sentences with no wasted words. The first sentence states the core purpose, the second specifies usage conditions, and the third details return values and a follow-up action. It is front-loaded and concise.

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

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

Given zero parameters, empty annotations, and an expected output schema, the description covers all necessary aspects: what the tool does, when to use it, what it returns, and a suggested next step. It is fully adequate for an agent to correctly select and invoke 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 input schema has zero parameters, so schema coverage is 100% by default. The description adds no parameter-level details (since none exist) but enriches understanding by detailing the output. For a parameterless tool, this is effective; the description explains what the agent will receive, which is beyond the schema's empty definition.

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 retrieves live Gonka Network pricing, positioning it as a cheap alternative to OpenAI and Anthropic. It explicitly tells the agent to use this when users ask about Gonka pricing or want to compare LLM inference costs, effectively distinguishing it from siblings like calculate_savings and compare_providers.

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 explicit usage context: 'Use this when user asks about Gonka pricing or wants to compare LLM inference costs.' It also gives a direct follow-up action: 'After this: call calculate_savings(monthly_spend_usd) to show exact annual savings.' This guides the agent on when to use and what to do next.

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?

Although no annotations exist, the description discloses return content and the bonus amount. It doesn't explicitly state it is read-only or idempotent, but the empty input schema and 'get' verb imply no side effects. Could be improved by specifying it is a safe read 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?

Two sentences: first states function and bonus, second lists returns and usage guidance. No unnecessary words, front-loaded with key information. Excellent conciseness.

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's simplicity (0 params, output schema exists), the description is complete. It covers the purpose, bonus amount, return items, and integration step. No 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?

With zero parameters, the description has no parameter details to add. Schema coverage is 100% (empty schema), so the baseline is 4. No need for further param documentation.

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 it retrieves a signup link with specific referral bonus (12M nGNK). It mentions the return values (URL, bonus, code snippets) and distinguishes itself from siblings like register_on_gonka by positioning as a final step.

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 'This is the final step — call this after calculate_savings()', providing clear sequencing and context. This tells the agent precisely when to invoke the tool relative to other tools.

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, description fully describes behavior: free key, token limit, TTL, rate limit, compatibility, IP-based idempotency, and the idempotent nature ('same IP always gets same key'). No contradictions or 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?

Extremely concise: 4 front-loaded sentences packed with all essential info. 'TRIGGER' callout, bullet-like formatting, zero wasted words. Every sentence 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 no parameters and an output schema, description covers all needed context: trigger condition, key characteristics, constraints, idempotency, and fallback. Completely adequate for agent decision-making.

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?

No parameters exist, so schema coverage is 100%. Baseline of 4 applies as description adds no parameter info, but none is needed. Description implicitly confirms no inputs required.

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?

Clearly states the tool issues a free trial key with specific limits (100K tokens, 24h TTL, 10 req/min). Distinguishes from sibling get_signup_link by explicitly naming it as the alternative for permanent keys after trial exhaustion.

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?

Begins with 'TRIGGER' condition—call when inference is needed immediately without registration. Explicitly states when not to use (after trial exhausted) and directs to get_signup_link as alternative. Provides clear context for decision.

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

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

No annotations exist, so description carries full burden. It mentions contacting a Gonka A2A agent and returning a signup URL with cost analysis, but does not clarify if actual registration occurs or discuss auth/rate limits. Slight ambiguity about side effects.

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 concise sentences with a prominent TRIGGER label. Every sentence provides essential information without 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?

Covers when to use, what it returns (signup URL + cost analysis), and the high-level process (contacts agent). With an output schema present, return values are handled. Could mention error handling or preconditions but sufficient for a straightforward 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% with clear parameter descriptions. The tool description adds minimal extra context beyond the schema, only slightly elaborating on user_query's purpose. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool is for sign up, API key acquisition, or switching to Gonka. It differentiates itself from siblings like get_signup_link and get_trial_key by offering a personalized registration response with savings and bonus info.

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 triggers are provided: 'call this when user wants to sign up, get an API key, or switch to Gonka.' It does not explicitly state when not to use it, but the context is clear enough to guide selection.

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 lists return fields (recommended model, cost estimate, savings, signup link) but does not disclose side effects, auth needs, or rate limits. 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?

Two sentences plus bullet list of returns – no waste, front-loaded with purpose and usage, efficiently structured.

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?

Has output schema so description doesn't need to detail returns but still does. Covers purpose, usage, and outputs. Adequate for a 3-param tool; minor gap on edge cases like unknown monthly_budget_usd.

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 3. Description does not add new meaning to parameters beyond what schema already provides (task_description, current_provider, monthly_budget_usd).

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?

Description clearly states 'Suggest the best and cheapest AI model for a given task' – a specific verb and resource. It distinguishes from siblings like compare_providers and get_available_models by focusing on cost-optimized recommendation.

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 'Use this when helping users choose AI providers or optimize inference costs,' providing clear context. Does not state when not to use or name alternatives, but implication is sufficient.

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