GrowVib: Social Media Growth

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context: it places no order and charges nothing, prices via the same server-side path as a real order, and explains that the response includes payment_methods with account vs. x402 options. This goes beyond the annotations and helps the agent understand side effects and response structure.

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 well-structured, with the core purpose front-loaded. It is a bit lengthy due to detailed payment method explanation, but every sentence adds value (pricing source, no-order guarantee, payment options). It could be slightly tighter, but the structure is logical and readable.

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 read-only quote tool with 3 parameters and no output schema, the description is comprehensive. It covers purpose, safety, pricing accuracy, and even details the payment_methods response field. An agent has everything needed to correctly invoke it and interpret results, especially given the supporting annotations.

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 descriptions for each parameter (service_id from search_catalog, quantity, optional service_option_id). The description does not add much beyond the schema, but none is needed because the schema is self-explanatory. It does mention 'quantity bounds enforced' which slightly reinforces the quantity parameter's meaning, but this is minor.

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

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

The description clearly states the tool's purpose: 'Get the exact list price to order a service and quantity' and immediately distinguishes it from order placement by noting 'It places no order and charges nothing.' It also contrasts with catalog math, which differentiates it from other catalog-related tools like search_catalog.

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 instructs to use this tool when an exact price is needed and to trust it over manual calculation from catalog prices. It also specifies that no account is needed and points to alternatives for actual ordering (place_order, place_paid_order) and mentions the REST endpoint for x402. This provides clear usage context and 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description doesn't need to re-state safety. It adds behavioral value by disclosing that the response includes ALL plan options and the specific option fields, and that the tool is a lookup step in a workflow. 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 dense but well-structured: the main capability is stated first, followed by expected workflow and an explicit alternative. The sentence is long, but every clause adds useful routing or content information without repetition.

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

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

Given a single required parameter, a rich input schema, detailed response field enumeration in the description, and annotations covering side effects, there is no missing context an agent needs to invoke this tool. It even defines how the result should be used in the larger order flow.

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 schema already covers service_id 100%, but the description adds meaning by specifying that the id comes from search_catalog and can be used to fetch a service the agent already has. This clarifies the provenance and usage of the parameter beyond the schema's bare 'The service id from search_catalog.'

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 and resource ('Get one service's full detail') and enumerates the exact fields returned (plan id, name, price per 1000, min/max quantity, quality tier, typed axes, delivery speed, start window, recommended flag). It distinguishes itself from recommend_service by explicitly defining its scope as showing all options with the chosen option passed downstream.

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?

States precisely when to use it: after search_catalog or whenever you already have a service id. It also explains what to do next (pass service_option_id to get_quote and place_order) and when NOT to use it: 'For a ready-made choice for a given quantity, call recommend_service instead.' This is exemplary routing among siblings.

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

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

Annotations already declare readOnly=true and idempotent=true, but the description adds meaningful behavioral context: polling is safe, and an order not belonging to the provided wallet is reported as not found (in the parameter description). Listing the possible statuses also sets expectations about the output. This exceeds what annotations alone provide 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 purpose, then parameter guidance, safety note, and status enumeration. Every sentence earns its place; there is no fluff or repetition. The structure allows an agent to quickly grasp what, how, and why.

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 there is no output schema, the description adequately describes what the tool returns: status, start count, and remaining quantity, and enumerates the possible statuses. It does not detail the exact response object structure, but for a status-checking tool, this is largely sufficient. Some edge cases (e.g., invalid order_id) are not covered, but the core information is present.

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?

Both parameters have schema descriptions, but the description adds extra guidance: how to obtain the agent_token (from place_paid_order or the auth challenge) and the security semantics (proves wallet ownership, non-owned orders appear not found). This supplements the schema meaningfully, so a score above the baseline of 3 is warranted.

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 tracks orders placed with place_paid_order, specifying the resource (an order) and the action (track status, start count, remaining quantity). It explicitly ties to the sibling place_paid_order, distinguishing it from other tools like search or recommend. The verb 'track' and the scoping to a specific order type make 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?

It provides explicit guidance on how to obtain and pass the agent_token, and states that polling is safe due to idempotency. While it doesn't explicitly exclude other tools, the context (tracking a specific order type) makes it clear when to use this tool. The mention of the authentication challenge endpoint adds useful context for non-place_paid_order tokens.

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 annotations already marking this as non-read-only, destructive, and idempotent, the description adds valuable behavior: the two-call x402 flow, network-specific signing, balance carryover, and the explicit retry/duplicate-payment warning. This is exactly the operational detail an agent needs.

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 dense and purposeful, front-loading the two-call protocol and key warnings. It is somewhat long, but every sentence carries operational value; the final get_quote mention is a useful brief alternative pointer.

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 the multi-step protocol, network-specific signing, overpayment behavior, retry safety, and when to use a sibling tool. With rich annotations and complete schema coverage, nothing essential is missing for a correct call.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description adds critical integration semantics: how and when to pass `payment`, how `agent_token` substitutes for it, and how `idempotency_key` prevents duplicate orders. This lifts the score above 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 identifies the verb (place/pay), resource (orders via x402), and exact scope (USDC on Base/Solana, no account required). It also distinguishes itself from get_quote by explicitly saying to call get_quote if the user only wants a price, which separates it from the sibling tools.

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 step-by-step usage: call once without payment to get requirements, then call again with the signed payment payload. It also gives exclusions and alternatives ('Use get_quote first if you only want a price') and warns against sending duplicate payments while confirmation is pending.

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 readOnly, idempotent, and non-destructive, and the description aligns with that by saying 'Read-only: it places no order.' On top of that, it discloses subtle behaviors: quoted_at is not a price hold, place_order re-prices from the same data, reasons state only positive attributes so absence is never a drawback, and tradeoffs uses null when one side lists nothing.

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 the tool is complex and has no output schema, so the density is justified. It front-loads the core output and then groups caveats and usage rules coherently, though a couple of clauses could be tightened without losing 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?

There is no output schema, so the description must carry return-value meaning, and it does: it enumerates returned fields, rejection reasons, quoted_at, catalog_version, tradeoffs deltas, and the sensitive flag. Given the seven parameters and five sibling tools, an agent has everything needed to invoke it correctly and interpret the result.

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, but the description adds meaningful cross-parameter semantics: service_id can be replaced by platform+goal, quantity must be accepted by every pick, and priority is defined in terms of LISTED delivery rate then start window with a fallback to balanced. This goes beyond the schema's per-parameter descriptions.

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 a specific verb+resource ('Recommend which plan option to order') and details the output: one recommendation plus the cheapest, highest-quality and fastest-listed alternatives. It clearly differentiates from get_service by saying 'Prefer this over reasoning across every option from get_service' and by specifying the option_id hand-off to get_quote and place_order.

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 tells the agent exactly when to use this tool: to choose and explain a choice, and to avoid reasoning over every option from get_service. It also gives identification alternatives (service_id OR platform + goal), the follow-up workflow (re-call and compare catalog_version before ordering), and explicit prohibitions such as never presenting a pick as a percentage more expensive than another.

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 the operation is read-only, idempotent, and non-destructive. The description adds genuinely useful behavioral context beyond that: the response contains both service-level and plan-level objects, and the agent should not silently choose the cheapest plan.

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 the purpose and remains dense but organized. The workflow note and get_service comparison are useful, though the parenthetical is slightly long and the 'place_order' naming slip prevents a perfect structure score.

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 no output schema and 12 optional parameters, the description does a good job explaining the return shape and the critical follow-up behavior. It is nearly complete, but the incorrect downstream tool name and lack of explicit mention of pagination in the description keep it short of excellent.

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 schema already documents all 12 parameters well. The description reinforces terms like 'starting/cheapest price per 1000' and 'min/max quantity', but it does not add substantial new parameter-level meaning beyond what the schema provides.

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

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

The description uses a specific verb ('Search') and names the resource ('sellable social growth service catalog') with platform examples. It also distinguishes itself from get_service, explicitly noting that get_service returns the same detail for a single known id.

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 gives clear workflow guidance: present plan options, let the user choose, and pass the selected service_option_id onward rather than defaulting to the cheapest. It also points to get_service for known IDs. However, it references 'place_order' while the actual sibling tool is place_paid_order, so one routing detail is slightly inaccurate.

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