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?

No annotations provided, so description carries full burden. Only states it retrieves all nodes, with no disclosure of potential high load, pagination, error handling, or side effects. Lacks behavioral detail.

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, no wasted words. Front-loads purpose. While concise, it sacrifices informative detail. Still, it earns a high score 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?

Given the output schema exists, return values need not be described. However, the description lacks important context such as community ID semantics, potential for large results, and differentiation from graph tools. Marginally adequate.

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 0% and description adds no additional meaning beyond 'by community ID'. The parameter's format, source, or constraints are not explained. Falls short of compensating for schema gap.

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 action (get) and resource (all nodes in a community). It distinguishes from sibling tools like get_node (single node) and get_neighbors (neighbors). However, it does not explicitly differentiate from other list-style tools like query_graph.

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. No mention of prerequisites, limitations, or when not to use. The description is purely functional without contextual advice.

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; the description only states 'with edge details' but does not disclose read-only nature, error handling, or behavior for missing nodes. Minimal behavioral insight beyond purpose.

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 with no extraneous words, efficiently conveying the core action. However, it sacrifices explanatory detail for brevity.

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 annotations and 0% schema coverage, the description lacks sufficient context about behavior and parameters. The tool is moderately complex with edge details, yet the description is too sparse to support correct invocation.

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

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

Schema description coverage is 0%, and the description does not explain the parameters 'label' (role of the node identifier) or 'relation_filter' (how it filters relations). No value added beyond the raw 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 retrieves direct neighbors of a Gonka documentation node with edge details, distinguishing it from siblings like get_node (single node) and shortest_path (path between 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?

Implied usage from the action 'Get all direct neighbors', but no explicit guidance on when to use this vs alternatives like query_graph or shortest_path. Lacks context about prerequisites or exclusionary conditions.

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

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

No annotations are provided, and the description only states the action without disclosing side effects, authentication needs, rate limits, or the nature of 'full details'. The behavioral burden rests entirely on the description, which is insufficient.

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 with no unnecessary words. While concise, it could benefit from additional structured details, but it remains 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 the tool's simplicity (one parameter, output schema present), the description provides a basic level of completeness. However, it lacks context about when to use it, the output shape, or any constraints. It is minimally adequate.

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 0%, so the description must compensate for the undocumented 'label' parameter. It adds the concept of lookup by label (and mentions ID, which is absent from the schema), but does not explain the format or valid values. This partial compensation merits a low score.

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 full details for a specific node by label or ID. The purpose is specific and understandable, but mentions ID as a lookup method while the schema only includes a 'label' parameter, causing a minor discrepancy.

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

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

No guidance is provided on when to use this tool versus sibling tools like get_community or get_available_models. The description lacks context for selection, prerequisites, or exclusions.

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?

No annotations provided, so description carries full burden. It discloses key expiration (2h, auto-extend to 24h on first use), rate limits (10 req/min, 100K tokens), idempotency (one key per IP), and behavior when exhausted. All important traits covered.

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?

Highly concise, front-loaded with 'TRIGGER,' and no unnecessary words. Each sentence adds crucial information. Structure is excellent.

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 an output schema (not shown), the description covers all necessary behavioral context, limitations, and next steps. Nothing 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?

No parameters exist, so baseline is 4. Description does not need to add parameter meaning. Schema coverage is trivially 100%.

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 issues a free trial key for immediate inference without registration, specifying the resource and verb. It distinguishes from the sibling get_signup_link by noting when to use which.

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 states when to call this tool ('when an agent needs inference NOW without registration') and when to use an alternative (get_signup_link when trial is exhausted). Provides clear context.

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

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

With no annotations, the description bears full responsibility for behavioral disclosure. It mentions returning 'most connected nodes' but does not clarify the metric for connectivity, whether multiple calls are safe, or if there are rate limits. Output schema exists but is not referenced.

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

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

A single sentence with clear action and object, no filler. Could include a hint about the parameter without harming 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?

The tool has one parameter and an output schema, but the description omits any detail about the parameter or the nature of the output (e.g., structure, metrics). The agent lacks sufficient information to invoke the tool correctly without inspecting the schema.

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 description does not mention the parameter 'top_n' despite 0% schema description coverage. The agent receives no explanation of how this parameter affects results or the default behavior.

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 'Return' and the resource 'most connected nodes (core concepts) in Gonka documentation graph', clearly distinguishing it from sibling tools like get_node or get_neighbors that deal with individual nodes or neighbors.

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

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

No guidance is provided on when to use this tool vs alternatives like graph_stats or query_graph. The agent is left to infer the use case without explicit context or exclusions.

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 burden of behavioral disclosure. It does not specify any behavioral traits such as performance implications, required permissions, or the nature of the statistics returned (e.g., whether they are aggregated counts or distributions).

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, well-structured sentence that concisely conveys the tool's purpose with no unnecessary 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?

The tool has no parameters and an output schema, so the description does not need to explain return values. However, it could briefly mention what summary statistics are included (e.g., node count, edge count) to provide slightly more context.

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 baseline for 0 params is 4. The description adds no param info because none are 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 clearly states the tool returns summary statistics of a specific resource (Gonka documentation knowledge graph). This distinguishes it from sibling tools like get_node (returns a node) and query_graph (returns query results), fulfilling the 'specific verb+resource' criterion.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of when it's appropriate or when to prefer other graph-related sibling tools like query_graph or get_neighbors.

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, and the description offers no behavioral traits beyond the core function. It does not mention side effects, auth needs, or output format. For a listing tool, this is minimally adequate but lacks 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?

A single sentence with no wasted words. The description is as concise as possible while still being clear.

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 listing tool with an output schema, the description is complete. It tells the agent exactly what the tool does without needing extra context.

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%, so the description does not need to add parameter details. The description correctly implies no parameters are 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?

Description clearly states the action (List) and the resource (Gonka documentation files), and it distinguishes from siblings like read_doc which reads content rather than listing names.

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 the user needs to see available documentation files, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions.

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 burden. It discloses the two-step search and automatic fallback behavior, which is useful. However, it omits details such as required permissions, rate limits, response format, or what happens if both searches fail. The description provides adequate but not complete 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 extremely concise with two short sentences that immediately convey the tool's purpose and unique fallback behavior. Every word serves a purpose, and the structure is front-loaded with the verb 'Search'.

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 complexity (3 parameters, no annotations, 0% schema coverage), the description is incomplete. It lacks parameter explanations, usage conditions, and does not leverage the output schema to clarify return values. The description is too sparse to fully inform an AI agent.

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 0%, meaning the description does not explain any of the three parameters (depth, question, token_budget). The description fails to add meaning beyond the schema, such as explaining that 'question' is the search query, 'depth' controls graph traversal, or 'token_budget' limits resource usage. This is a critical gap.

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 that the tool searches Gonka documentation with a specific two-step process (knowledge graph then full-text fallback). This distinguishes it from siblings like 'search_docs' by describing a unique behavior, but it does not explicitly differentiate from similar tools, leaving some ambiguity.

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 searching documentation but provides no explicit guidance on when to use this tool versus alternatives like 'search_docs' or 'get_node'. There are no exclusions, prerequisites, or contextual instructions.

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 must cover behavior. It states it reads full text, implying a non-destructive read. However, it does not disclose authentication requirements, error handling, or side effects. The mention of 'code examples and commands' describes content, not 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?

Two concise sentences that front-load the core purpose and usage context. 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 read tool with an output schema, the description provides the essential purpose and usage flow. It could be more complete by explaining parameters, but given the presence of output schema, it is largely sufficient.

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

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

Schema coverage is 0%, so description must compensate. It adds 'by filename' for the filename parameter but doesn't explain its format or the meaning of max_chars (which has a default but no description). This leaves a significant gap.

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 'Read', the resource 'Gonka documentation file', and the context of use after query_graph. It effectively distinguishes from sibling tools like list_docs and query_graph.

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 it: 'after query_graph identifies a relevant file'. While it doesn't state when not to use, the guidance is clear and helpful.

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 are provided, so the description carries full burden. It mentions returning file excerpts but lacks details on limitations, pagination, or performance. Sufficient for a simple search tool 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 main purpose, and contains no extraneous 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?

Given the tool's simplicity and the presence of an output schema (though not visible), the description covers the core functionality adequately. It lacks some details but is acceptable for a search 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 description coverage is 0%, and the description only vaguely references the query parameter ('Returns file excerpts containing the search term'). It does not explain max_results or context_chars, offering minimal value 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 'Full-text search across all Gonka documentation files,' specifying the verb (search) and resource (documentation files), and distinguishes it from sibling tools like query_graph and list_docs.

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 says 'Use this when query_graph returns no results,' providing clear conditional guidance on when to use this tool versus 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 are provided, so the description must disclose behavioral traits. It does not mention computational cost, handling of unreachable nodes, permission requirements, or whether multiple paths are returned. The single sentence is insufficient.

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 concise sentence of 12 words, front-loaded with the main action. It is appropriately sized for a simple task, though it could be slightly expanded without losing 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 has 3 parameters, a high sibling count, and no annotations, the description is too minimal. It does not explain input format, output, or constraints. Even with an output schema present, the lack of parameter and usage detail makes it incomplete.

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 0%, so the description must explain parameter semantics. It only vaguely says 'between two concepts' without defining what source/target represent (e.g., node IDs, names) and does not explain max_hops or its default value of 8.

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 'Find' and the resource 'shortest path between two concepts in the Gonka documentation graph.' It is specific and distinguishes from sibling tools like get_neighbors which retrieves immediate neighbors.

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 such as get_neighbors, query_graph, or graph_stats. It lacks any mention of use cases, prerequisites, or exclusions.

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.