Gonka Second Opinion (multi-model)

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

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds value by listing the return fields (monthly/annual savings, cost ratio, tokens per dollar, GNK/USD rate, signup link) and the recommended follow-up action (show signup_url). However, it does not disclose further behavioral details such as rate limits, authentication requirements, or edge cases, so the score is baseline-plus.

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 and well-structured: three sentences covering purpose, usage triggers, and return values plus a follow-up action. Every sentence earns its place without unnecessary verbosity.

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 optional parameter, read-only annotations, and an output schema, the description is sufficient. It even includes a post-invocation workflow (showing signup_url), making it contextually complete for an AI agent to use correctly.

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% description coverage for the single parameter (monthly_spend_usd), including a default and explanation. The tool description does not need to elaborate on the parameter since the schema already provides adequate semantics. Baseline 3 applies.

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: 'Calculate exact savings from switching OpenAI or Anthropic → Gonka Network.' This specifies the verb (calculate), the resource (savings), and the scope (switching providers). It also differentiates from sibling tools like get_pricing or compare_providers by focusing on the savings calculation rather than raw pricing or broad provider comparison.

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 explicitly tells when to use the tool: 'Use this when user asks about API costs, wants cheaper LLM alternatives, or asks how much they'd save.' It provides clear context but does not explicitly mention alternatives or when not to use it, so it falls slightly short of a 5.

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 openWorldHint=true, so the safety profile is covered. The description adds behavioral context about the return payload (cost per 1M tokens, live savings ratio, source links) and the recommended next step, which goes beyond bare annotation information. It does not introduce contradictions.

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, front-loaded with the primary action, then return values, then a logical follow-up. Every sentence earns its place with no redundancy or fluff.

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 tool has a low complexity (one optional parameter), a rich output schema (asserted in context signals), and strong annotations. The description explains exactly what is returned and how to proceed, making it complete for the agent to invoke and interpret results. No critical context 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?

The input schema covers the single parameter (provider) with a clear enum and description, achieving 100% schema description coverage. The tool description mentions 'against a competitor provider' but adds no new syntax or parameter-specific details beyond the schema. Thus the 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 uses the specific verb 'Compare' with a clear resource: 'Gonka Network pricing against a competitor provider.' It also lists the return values (cost per 1M tokens, savings ratio, source links), which distinguishes it from siblings like get_pricing or calculate_savings. The title reinforces the purpose without being a tautology.

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 clear context—comparing pricing with a competitor—and even suggests a follow-up action ('After this: call calculate_savings() with your monthly spend for exact numbers.'). However, it does not explicitly contrast with sibling tools like get_pricing or state when not to use this tool, so it lacks explicit exclusions or alternative naming.

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 and idempotentHint=true, so safety and idempotency are covered. The description adds no additional behavioral details beyond its purpose; this is acceptable given the strong 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?

The description is a single, focused sentence (19 words) that states the purpose and a use case without any redundancy. It is well-structured and 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?

Given the presence of an output schema, complete parameter descriptions, and annotations clarifying read-only and closed-world behavior, the description provides enough context for an agent to select and invoke the tool correctly. No additional return-value details are necessary.

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 clear parameter descriptions (e.g., 'Starting concept name', 'Destination concept name', 'Give up if the path is longer than this many edges'). The description does not add parameter-level information, but the schema alone is sufficient.

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 ('Find') and clearly identifies the resource ('Gonka documentation concepts') and the goal ('how two ... are connected'). It also provides a concrete use case ('how does X relate to Y'), which differentiates it from sibling tools like get_neighbors or get_node.

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 explicitly states when the tool is useful ('answering how does X relate to Y questions'), giving clear context for use. It does not mention alternatives or exclusion cases, but the stated use case is sufficient.

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, openWorldHint=true, and idempotentHint=true. The description adds beyond that: 'live pricing' implies real-time data (not static), and 'Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls' is a key behavioral trait for API invocation. It also discloses the return format (model IDs usable directly in openai.chat.completions.create, status, USD per 1M tokens), which enriches the agent's expectation of the tool's output.

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 four sentences, front-loaded with purpose, then usage, then return format, then next-step guidance. Every sentence earns its place: no redundancy, no filler, and the structure logically progresses from what → when → output → follow-up. It is concise yet information-dense.

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 (zero params, output schema present, strong annotations), the description is complete. It covers purpose, usage context, behavioral compatibility, output fields, and a suggested subsequent action. The presence of an output schema means detailed return documentation isn't necessary, but the description still provides a useful summary. No gaps remain for an agent to select and invoke this tool correctly.

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, so the schema fully covers all parameters (vacuously). The description adds no parameter-specific information because none is needed. The baseline for zero-parameter tools is 4, and the description appropriately focuses on usage and output rather than inventing parameter detail.

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 opens with a specific verb and resource: 'List all AI models available on Gonka Network with live pricing.' It clearly states the deliverable (model IDs, status, USD per 1M tokens) and differentiates from siblings by emphasizing live pricing and drop-in OpenAI/Anthropic compatibility. The use case ('when user asks which model to use or wants alternatives to GPT-4o / Claude') further distinguishes it from tools like calculate_savings or 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 explicitly states when to use the tool: 'Use this when user asks which model to use or wants alternatives to GPT-4o / Claude.' It also provides a workflow directive ('After this: call calculate_savings() to see annual savings with these models'), which is actionable guidance. While it doesn't list 'when not to use' scenarios, the positive guidance and chaining to a sibling make the usage intent unambiguous.

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 openWorldHint=false, covering the safety profile. The description adds domain context (what a community is) but no additional behavioral traits such as side effects, prerequisites, or limits. No contradiction found.

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?

One sentence, about 30 words, front-loaded with the action and resource, followed by a concise parenthetical clarification. No wasted 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?

For a simple one-parameter read-only tool with a detailed parameter schema and output schema, the description sufficiently defines the scope (all concepts in a community) and provides illustrative examples. It could be more complete with explicit usage guidance, but it is not lacking in essential information.

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 for the single parameter is 100%, with a detailed description explaining that community_id comes from the 'community' field of other graph functions. The tool description itself does not add extra parameter-level meaning, so it stays at the baseline.

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 resource ('all concepts belonging to one documentation community'), with a helpful parenthetical defining what a community is and giving concrete examples ('wallet-related or node-operation concepts'). This distinguishes it from sibling tools like get_node (single node) and get_neighbors (adjacent nodes).

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: you call this when you have a community_id and want all concepts in that community. However, it does not explicitly mention when not to use it or name alternatives, relying on the user to infer distinctions from the sibling list.

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 openWorldHint=false, so the agent knows this is a safe, deterministic read operation. The description adds that it returns ranked concepts and is a summary overview, but does not disclose output format or edge cases. This is consistent with annotations and adds modest value.

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, front-loaded sentence that begins with the action ('Return the most-referenced concepts') and only adds relevant context about when to use it. Every word earns its place, with no redundancy or filler.

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

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

For a simple read-only tool with an output schema, one optional parameter, and strong annotations, the description is nearly complete. It states the purpose and use case, and the schema covers top_n. It might benefit from mentioning whether it returns only concept names or full details, but this is not necessary given the output schema exists.

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%: the single parameter top_n is fully described in the schema ('How many concepts to return, ranked by number of connections'). The description does not add any parameter-specific guidance beyond what the schema already provides, so the baseline of 3 applies.

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 returns the most-referenced concepts in the Gonka documentation graph, providing a specific verb+resource. It distinguishes from siblings like get_node and get_neighbors by focusing on a global overview rather than node-specific operations, and gives concrete examples (architecture, collateral, inference).

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 clear usage context: 'when you don't know where to start' and describes it as a quick overview of core topics. It implies this is an entry-point tool for orientation, but does not explicitly name alternative tools or when not to use it. The context is sufficient for a basic understanding.

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 declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the agent already knows this is a safe, read-only operation. The description adds no extra behavioral context beyond the fact that it returns aggregate statistics, such as data freshness, whether stats are cached, or any rate limits. It does not contradict annotations, so a score of 3 is appropriate.

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 immediately states what the tool does. It is front-loaded and contains no filler or redundant information.

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, no-parameter, read-only tool with an output schema present, the description is mostly sufficient. However, it does not specify what 'summary statistics' includes (e.g., node count, edge count), leaving some ambiguity. The output schema likely covers return values, so this is a minor gap rather than a critical one.

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% (there is nothing to cover). The description does not need to explain parameters. The baseline of 4 applies because there is no parameter information to add.

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 ('Return summary statistics') and identifies the exact resource ('Gonka documentation knowledge graph'). It clearly differentiates from sibling tools like query_graph or get_node, which perform detailed traversals or lookups rather than aggregating whole-graph stats.

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 no guidance on when to use this tool versus alternatives. It does not mention that this is an overview tool or suggest using query_graph for detailed graph exploration. No exclusions or alternative tool references are given.

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 adds value beyond the read-only and idempotent annotations by specifying the exact output content (relation type and confidence of each edge). This gives the agent a concrete expectation of what the tool returns, though it omits any potential edge cases like pagination or 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 two sentences long, with the first sentence immediately stating the primary function and output. The second sentence provides usage context. Every word earns its place, with no redundancy or filler.

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

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

This is a simple 2-parameter tool with an output schema available. The description covers the purpose, usage context, and output content sufficiently. There is no need for further details about return values or edge cases, making the description complete for this complexity level.

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 'label' and 'relation_filter' are described in the input schema. The description does not add any parameter-specific details that are not already present, so the baseline score of 3 applies.

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 ('Get') and resource ('all concepts directly connected to a given concept'), and clarifies the output includes relation type and confidence. It also references sibling tools (query_graph, get_node) to distinguish from related exploration tools, making the purpose unmistakable.

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 explicitly states when to use this tool ('to explore what's related to a concept you already found via query_graph() or get_node()'), providing clear context. It does not mention when not to use it or name alternatives, but the guidance is sufficient for typical 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds no extra behavioral context (e.g., what 'full details' includes, behavior on missing concept). It does not contradict 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 sentence, front-loaded with the verb and resource, and contains no unnecessary words. It is appropriately concise for a simple lookup tool.

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

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

For a one-parameter, read-only, idempotent tool with an output schema, the description is completely sufficient. The agent knows what it does and can rely on the schema and annotations for details.

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 a thorough parameter description for 'label' including examples and guidance to use query_graph first. The tool description adds no additional meaning beyond the schema, 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 uses a specific verb ('Get full details') and resource ('Gonka documentation concept') with a clear method ('by name'). This distinguishes it from siblings like query_graph (which searches/finds nodes) and read_doc (which reads a document page). It is unambiguous 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?

The description implies usage when you have a specific concept name and need full details, but it does not explicitly state when to use it versus alternatives. The query_graph() guidance appears only in the schema's parameter description, not in the main description, so it is not credited here per the 'beyond structured fields' principle.

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, openWorldHint=true, and idempotentHint=true, establishing the safety profile. The description adds behavioral details beyond annotations: data is updated every 10 minutes, includes specific return items (USD per 1M tokens, GNK/USD price, savings ratios vs specific providers, gateways). This provides useful context about freshness and scope without contradicting the 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 three sentences, each serving a distinct purpose: what it does, when to use it, and what it returns with a next step. It is front-loaded with the core purpose and contains zero redundant or vague phrasing. Perfectly sized for a tool with no 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 tool's simplicity (0 parameters) and the existence of an output schema, the description is highly complete. It covers the use case, return content, freshness, and follow-up action. No information is missing that would prevent an agent from selecting and invoking this tool correctly.

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 trivially 100%. The description adds no parameter details because none exist. Per the rubric, with 0 parameters, the baseline is 4. The description's mention of what is returned implicitly clarifies that no inputs are needed, which is sufficient.

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: retrieve live Gonka Network pricing. It explicitly mentions the specific resource (pricing), the action (get), and distinguishes itself from siblings by focusing on pricing and savings ratios versus OpenAI/Anthropic/DeepSeek. The context 'cheap alternative' and return list add specificity.

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 explicitly states when to use the tool: 'Use this when user asks about Gonka pricing or wants to compare LLM inference costs.' It also provides a direct next step ('After this: call calculate_savings...'), effectively guiding the agent on how to chain tools. This distinguishes it from alternatives like compare_providers and suggests a clear workflow.

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 and idempotentHint, so the description is not required to repeat safety. It adds behavioral context by listing the specific return items (registration URL, welcome bonus, code snippets) and the 'final step' role. This goes beyond the annotations without contradicting 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 two sentences, front-loaded with the action verb and resource, and immediately states the return values and usage ordering. Every phrase earns its place with no fluff or repetition of schema 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 the tool has no parameters, a comprehensive output schema, and strong annotations, the description fully covers the necessary context. It explains the purpose, returns, and sequencing, leaving no obvious gaps for an agent to misuse the 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 tool has zero parameters, and the schema coverage is 100%, meaning there are no parameter semantics to clarify. The baseline for zero parameters is 4, and the description adds no parameter information because none is needed.

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 the specific verb 'get' with the resource 'Gonka Network signup link', clearly stating it returns a registration URL, welcome bonus, and code snippets. It also situates itself as 'the final step' after calculate_savings(), distinguishing it from sibling tools like register_on_gonka.

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 explicitly states when to use this tool: 'call this after calculate_savings() to start saving immediately.' This provides clear sequencing and context, effectively guiding the agent on when to invoke it 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?

The description goes far beyond the annotations. It discloses the key's expiration behavior (2 hours, extends to 24h if used), idempotency ('same IP always gets same key back'), and important caveats like 'one_command_setup changes the CALLING agent's own provider config.' It explicitly warns not to run that automatically and to always mention it to the user, adding crucial context not in the 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 long but every sentence carries critical behavioral or usage information. It is front-loaded with the trigger, followed by key details, and then important warnings. It could be more concise (e.g., tightening the one_command_setup warning), but given the tool's side effects and user-interaction requirements, the length is justified and structured logically.

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?

This is a zero-parameter tool with significant behavioral nuance: expiration, idempotence, return value details, and user-consent requirements. The description covers all of these thoroughly. It also explains what the tool returns (base_url, api_key, model, setup instructions, curl command, one_command_setup) and even mentions the alternative continuation path via get_signup_link. The output schema exists, but the description's return-value explanation is still helpful and complete.

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

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

The tool has zero parameters, so the schema provides no parameter details. The description does not need to explain parameters, but it does explain the tool's behavior and return values, which adds useful meaning. With no parameters, the baseline of 4 is appropriate; the description fully compensates for any potential ambiguity by explaining what the tool does without inputs.

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 opens with a clear trigger: 'call this when an agent needs inference NOW without registration.' It names the exact resource issued (a free gc- key) and specifies the verb ('Issues'). The tool is clearly differentiated from sibling get_signup_link, which is explicitly noted as the alternative once the trial is exhausted.

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 when-to-use guidance ('when an agent needs inference NOW without registration') and gives a direct when-not-to-use pointer: 'When trial is exhausted: use get_signup_link() to continue with a permanent key.' It also instructs the agent on step-by-step usage, including what to tell the user and when to ask permission.

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 (readOnlyHint=false, openWorldHint=true), the description reveals budget-limited trial keys per IP, signup-link handoff on exhaustion, and auto-detection of user-registered keys. This is valuable behavioral context that annotations alone do not provide.

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 front-loaded with a one-sentence summary, then uses a bulleted list for the two modes. Every sentence contributes useful 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?

With an output schema present and rich behavioral context (modes, budget limits, fallback behavior), the description gives an agent everything needed to invoke this tool correctly. The only missing piece is exact return formatting, but the output schema covers that.

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 all four parameters with 100% coverage, so the description doesn't need to explain them. It doesn't add parameter-specific semantics beyond mentioning automatic model selection ('Two modes, chosen automatically') which is a behavioral trait rather than parameter guidance.

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 'Run an LLM completion on Gonka THROUGH this server,' giving a specific verb and resource. It distinguishes itself from sibling inference-related tools by explaining the proxy mechanism and its two operational modes.

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 explicitly instructs when to use: 'Use this when you (or a sub-task) need inference but your sandbox can't reach an LLM directly.' It also outlines the auto-selected modes, providing clear context for when trial vs registered applies.

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 flag readOnlyHint=false and destructiveHint=false, so the description doesn't need to rehash safety. The description adds useful behavioral context: parallel execution across models, returning each answer for comparison, and that the user's model stays in charge. It does not disclose potential side-effects such as the share option creating a public page, though the schema does. Given the annotation coverage, this is 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?

The description is two sentences, front-loaded with the core action, followed by concrete use cases and a reassurance about provider switching. Every word earns its place with no redundancy or filler.

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

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

The description covers the tool's function, use cases, and basic behavior, which is sufficient for an agent to decide when to invoke it. The schema and output schema handle parameter details and return structure. It omits the conditional sharing/publication side-effect and cost/time implications, but these are either in the schema or not essential for selection. It is nearly complete for a tool of this complexity.

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%, and the schema itself provides detailed explanations for parameters (e.g., share's public-link behavior, max_tokens being kept low, perspectives as role labels). The tool description does not need to repeat parameter semantics, thus the baseline of 3 applies. It adds no extra meaning beyond the schema but also doesn't create ambiguity.

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 action: 'Get a SECOND OPINION: run one prompt across Gonka models in parallel and return each answer for comparison.' It distinguishes itself from siblings like gonka_chat by emphasizing multi-model parallel execution and comparison, and clarifies the user's own model remains in charge. The purpose is unambiguous and resource-specific.

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 explicit use cases: 'sanity-check a decision, test a hypothesis, or see where other models/viewpoints agree or differ.' It also notes the benefit 'without switching your main provider.' However, it does not explicitly state when NOT to use this tool or name alternative tools, so it falls shy of a 5.

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 annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered. The description adds that the list includes 'all available' files, implying a comprehensive enumeration, but does not disclose return format, pagination, or potential edge cases. With annotations present, this minimal additional context is 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?

The description is a single sentence with no filler or redundant information. It front-loads the action and resource clearly, achieving maximum conciseness while remaining informative.

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, no-parameter, read-only listing tool, the description is sufficient. The output schema exists, so the return values are documented there. The description covers the essence of the operation, and no critical information seems missing for an agent to select and invoke the tool correctly.

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, so there is no parameter semantics to explain. The baseline for zero parameters is 4, and the description appropriately does not waste space on nonexistent parameters.

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: 'List all available Gonka documentation files.' It specifies the verb (List), the resource (documentation files), and the scope (all available). This distinguishes it from siblings like read_doc (which reads a specific file) and search_docs (which searches), though it does not explicitly name these alternatives.

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?

There is no guidance on when to use this tool versus alternatives. The description implies usage when needing to enumerate documentation files, but it does not state when not to use it or suggest other tools for specific cases. No explicit exclusions or comparisons to sibling tools are provided.

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 readOnlyHint and idempotentHint annotations, the description reveals valuable behavioral logic: first searches the knowledge graph, then automatically falls back to full-text search if nothing found. This exposes the tool's search strategy and fallback behavior, which is not inferable from annotations alone.

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: first states purpose and fallback behavior, second gives usage guidance. Every sentence earns its place and the most critical information is 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 tool is well-situated among siblings: it names read_doc and search_docs, explains the search algorithm, and the output schema covers return values. Given the annotations and schema richness, the description is complete for an agent to select and invoke this tool correctly.

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 provides complete descriptions for all three parameters (question, depth, token_budget), with 100% coverage. The description does not add additional parameter-level meaning, so it meets the baseline but does not exceed 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 'Search Gonka documentation' with a specific verb and resource. It also explicitly frames the tool as the primary entry point, distinguishing it from read_doc and search_docs by naming them directly.

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

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

Gives explicit directive: 'try this before read_doc or search_docs.' This is clear when-to-use guidance and names alternatives, making the decision process unambiguous.

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 and idempotentHint=true, so the description doesn't need to restate safety. It adds useful context that the content includes code examples and commands, and that filenames come from other tools. The phrase 'full text' might slightly overpromise given max_chars truncation, but the schema clearly documents the truncation 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 two sentences with no filler. The first sentence states the purpose and content, the second gives a usage rule. It is efficiently front-loaded and 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 that the tool is a simple read operation with a 2-param schema, 100% schema coverage, and an output schema, the description covers the essential purpose and usage guidance. The only minor gap is not mentioning truncation in the description itself, but the schema's max_chars description handles this, making the overall package complete enough.

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 baseline is 3. The tool description doesn't add parameter-specific meaning beyond what's in the schema, but the schema already provides clear descriptions for both 'filename' (with allowed sources) and 'max_chars' (with default and truncation behavior). No gap requiring compensation.

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 'Read the full text of a Gonka documentation file' with a specific verb and resource, and adds detail about code examples and commands. This clearly distinguishes it from sibling tools like list_docs or query_graph, which are about listing or searching files.

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 guidance says 'Use this after query_graph() or search_docs() identifies the relevant filename — don't guess a filename directly.' This tells the agent exactly when to use the tool and warns against a common misuse, effectively differentiating it from search/list 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?

Beyond the annotations (readOnlyHint=true, idempotentHint=true), the description discloses a critical non-obvious behavior: 'Does NOT register or create an account — it only computes savings... and returns the signup URL.' This prevents the agent from assuming the tool completes registration and adds value beyond the annotation hints. 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?

The description is three sentences long, starts with the core purpose, and immediately clarifies the non-registration aspect. Every sentence adds distinct value: purpose, non-behavior, and usage guidance. No redundancy or fluff.

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 moderate complexity, the description adequately covers the essential context: what it does, what it does not do, and when to use it. The output schema (not shown) likely covers return format specifics. The note that registration is left to the user is critical and complete. No major gaps identified.

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%, and each parameter (user_query, current_provider, monthly_spend_usd) is well-documented in the schema. The description adds only indirect references to 'spend/provider' but does not provide additional parameter-level meaning beyond what the schema already states. Thus the baseline 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's function: 'Get a personalized cost-analysis pitch and signup link for switching to Gonka.' It explicitly distinguishes itself from actual registration by saying 'Does NOT register or create an account,' and differentiates from siblings by combining cost analysis with a signup link, which no other sibling 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?

The description provides explicit usage context: 'Use this when a user wants to sign up, get an API key, or switch to Gonka.' It also clarifies the when-not: the tool does not perform registration, as the user 'still complete[s] registration themselves at the returned signup_url.' This directly guides an agent on when to select this tool over others.

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 indicate read-only and idempotent behavior. The description adds valuable behavioral context by explaining that it does an AND search (all words must match) and is case-insensitive, which goes beyond the standard annotations. It also clarifies that it searches across all documentation files, providing scope.

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 sentences long and efficiently conveys the core function, matching behavior, and usage context. It is front-loaded with the main purpose and does not waste words on redundant information. Every sentence contributes to understanding the tool.

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

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

Given the tool's simplicity, the presence of a thorough input schema, output schema, and clear annotations, the description is complete. It explains the search scope, matching rule, and provides a usage scenario. It does not need to explain return values because the output schema covers that.

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 baseline is 3. The description does not directly elaborate on parameters, but the schema already provides thorough descriptions for query, max_results, and context_chars. The tool description's mention of 'every word' reinforces the query parameter semantics, but it doesn't add significant new 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's function: 'Full-text search across all Gonka documentation files.' It also specifies the matching semantics (AND search, case-insensitive) and distinguishes it from query_graph by noting that it should be used when query_graph() returns no results, which effectively differentiates it from a key sibling.

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 explicitly tells when to use this tool: 'Use this when query_graph() returns no results.' This provides direct guidance versus a specific alternative. Additionally, the query parameter description advises preferring fewer, more specific words, which is useful usage 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=true and idempotentHint=true, covering the safety profile. The description adds the return value structure ('recommended model, live cost estimate, savings vs current provider, signup link') and highlights the 'live' nature of the cost estimate, which provides behavioral context beyond the 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 three sentences: purpose, usage, and return value. It is front-loaded with the most critical information and contains no redundant or filler 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?

For a recommendation tool with three parameters and an output schema, the description covers the essential context: what it does, when to use it, and what it returns. The output schema handles detailed return format, and annotations cover safety, making this description 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 description coverage is 100%, so the parameters are already fully documented. The description does not add additional semantic detail about parameters beyond what the schema provides; it only indirectly references current_provider via 'savings vs current provider.' 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 opens with a specific verb ('Suggest') and resource ('best and cheapest AI model for a given task'), clearly stating the tool's core function. This distinguishes it from sibling tools like get_available_models or get_pricing by focusing on a recommendation that combines quality and cost.

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 second sentence provides a clear usage scenario: 'Use this when helping users choose AI providers or optimize inference costs.' While it does not explicitly name alternative tools or when-not-to-use conditions, the context is specific enough to guide selection among the listed siblings.

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