Gonka Network Pricing
Server Details
Compare LLM inference costs vs OpenAI/Anthropic/DeepSeek. Gonka is up to 6800x cheaper.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool access control
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.8/5 across 5 of 5 tools scored. Lowest: 2.9/5.
Each tool targets a distinct task: calculating savings, comparing providers, listing models, retrieving pricing data, and getting signup link. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern with underscores (e.g., calculate_savings, get_pricing), making them predictable and easy to distinguish.
With 5 tools, the server is well-scoped for a pricing-focused service. The number is neither too small nor too large for the domain.
The tool set covers core pricing operations: retrieving prices, listing models, comparing providers, and calculating savings. There is minor overlap between compare_providers and get_pricing (the latter already includes comparison ratios), but overall the surface is sufficient for typical pricing inquiries.
Available Tools
5 toolscalculate_savingsAInspect
Calculate how much you would save by switching from OpenAI to Gonka Network.
| Name | Required | Description | Default |
|---|---|---|---|
| monthly_spend_usd | Yes | Your current monthly OpenAI API spend in USD |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It merely states the purpose without disclosing any behavioral traits (e.g., assumptions, accuracy, or whether it's an estimate).
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, clear sentence that conveys the tool's purpose without any superfluous words.
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 only one parameter and an output schema present, the description is largely sufficient. However, it could mention that the result is an estimate based on current pricing.
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 single parameter is fully described in the schema (100% coverage), and the description adds no extra meaning 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 calculates savings from switching to Gonka Network. It uses a specific verb and resource, and implicitly distinguishes from siblings like compare_providers or 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?
The description implies when to use (to calculate savings), but offers no guidance on when not to use or how it differs from sibling tools like compare_providers or get_pricing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_providersCInspect
Compare Gonka Network pricing against a competitor.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No | One of "openai", "anthropic", "deepseek" (default: "openai") | openai |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behaviors. It only says 'compare pricing' without mentioning side effects, authentication needs, or output format (though an output schema exists). This is insufficient for a rating higher than 2.
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, and concise. It could include more detail without becoming verbose, but it earns a 4 for efficiency.
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?
Despite an output schema, the description lacks important context: what does 'compare' produce? (e.g., price differences, savings). Behavioral and usage gaps make it incomplete for a low-complexity 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% (provider parameter described with options). The description adds no extra meaning beyond 'against a competitor', so 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 compares pricing against a competitor, with the verb 'compare' and resource 'pricing'. It differentiates from siblings like get_pricing and calculate_savings, though it could be more specific about the comparison output.
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 on when to use this tool versus alternatives like calculate_savings or get_pricing. The description only states the action without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_available_modelsAInspect
List models available on Gonka Network.
Returns each model's:
ID (same as used in OpenAI API calls)
Availability status (available / restricted)
Notes on access requirements
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries burden. It discloses output fields (ID, status, notes) but does not mention side effects, caching, or authentication requirements. The mention of 'available / restricted' adds some 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?
Two sentences with bullet points, no wasted words. Front-loaded with purpose, then output details. Maximally 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?
For a parameterless list tool with an output schema, the description fully covers what the tool does and what is returned. No additional context needed.
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 0 parameters, so baseline is 4. The description adds value by explaining the return structure (ID, status, notes), which goes beyond the empty 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 models available on Gonka Network and details the returned fields. It is specific with a verb and resource, and distinguishes itself from siblings like calculate_savings or 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?
No guidance on when to use this tool versus alternatives. The description does not provide context about when to use it or mention exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pricingAInspect
Get current Gonka Network pricing data.
Returns live pricing including:
Cost per 1M tokens in USD and GNK
Current GNK/USD exchange rate
Comparison ratios vs OpenAI, Anthropic, DeepSeek
$50 deposit example: how many tokens you get
Data freshness timestamp
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses return fields (cost per 1M tokens, exchange rate, comparison ratios, deposit example) and data freshness timestamp, giving the agent a clear understanding of the tool's output and behavior.
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 with a single introductory sentence followed by a bullet list, making it front-loaded and easy to scan. Every sentence 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?
Given zero parameters and the presence of an output schema (which reduces the need for return value descriptions), the description is complete. It explains the specific pricing information returned, which is sufficient for an agent to understand the tool's utility.
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 the input schema coverage is 100%. The description adds no parameter information, which is appropriate since none are needed. Baseline 4 for no parameters is exceeded due to clear output specification.
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 'Get' and the resource 'current Gonka Network pricing data', listing specific output fields which distinguishes it from sibling tools like calculate_savings or compare_providers that have 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 description implies use for retrieving pricing data but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. The context signals list siblings, which helps differentiation, but the description itself lacks exclusionary advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_signup_linkAInspect
Get the Gonka Network signup link with referral bonus.
Returns the registration URL, welcome bonus details, and quick-start code snippet for connecting with the OpenAI SDK.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It states the output but does not mention that the operation is read-only, any authorization needs, or rate limits. The behavioral transparency is adequate but not thorough.
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, no redundancy. The first sentence captures the main action and resource. The second lists return values concisely. Every part is necessary and clearly 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?
Given the tool has no parameters and an output schema exists, the description is mostly complete. It explains the tool's purpose and output. It could note that the operation is read-only, but the context is sufficient for an agent to use the tool appropriately.
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 no parameters (100% coverage vacuously). The description adds value by listing the returned items (URL, bonus, code snippet), providing context beyond the schema. A score of 4 reflects that no parameter documentation is needed and the description compensates well.
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: retrieving the Gonka Network signup link with referral bonus. It specifies the output contents (URL, bonus details, code snippet), distinguishing it from sibling tools that deal with pricing or savings.
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 for signup-related tasks, but does not explicitly state when to use this tool versus alternatives. It lacks when-not-to-use guidance or references to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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