MCPVOT — x402 Facilitator Machine

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value beyond these by noting the endpoint is 'FREE (no payment required)' and enumerating the specific pieces of information returned (server health, x402 services, MCP endpoint info).

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 short sentences, front-loaded with the core purpose. Every word adds value, and the 'FREE endpoint' note is a meaningful complement to the purpose.

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 low complexity (no parameters, no output schema), the description fully captures its functionality and constraints. It clearly states what the user gets and that no payment is required, making it complete for the tool's simplicity.

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 baseline is 4 per the rubric. The description correctly omits parameter details because there are none, and it still communicates what the tool returns.

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 with a specific verb ('Get') and resource ('mcpvot.xyz server health, available x402 services, and MCP endpoint info'). It is immediately distinct from sibling tools, which focus on racing data or web search.

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 tool's purpose is obvious from the description, and given the unrelated sibling names, the intended use case (checking server/ecosystem status) is clear. However, no explicit 'when to use' or alternative comparisons are provided, though the context makes exclusion unnecessary.

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 non-destructive. Description adds useful behavioral context: cost per call and subscription requirement, which annotations do not cover.

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 defines output content, second gives 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 single-parameter tool with no output schema and good annotations, the description adequately covers purpose, input source, prerequisites, and cost. Could mention result format or limits, but not essential.

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 the single parameter. Description adds cross-tool context ('from search_hosted_races'), providing 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?

Clearly states it returns full iRacing race results including finish order, laps, incidents, iRating, and weather. Distinguishes from sibling search_hosted_races which provides the subsession_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?

Explicitly states it takes a subsession_id from search_hosted_races, requires active subscription, and costs $0.02. Does not explicitly list when not to use but context 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?

Annotations already indicate read-only, non-destructive behavior. The description adds valuable context by noting the subscription requirement and per-call cost, which are beyond annotation scope. However, it omits details like error cases or rate limits, which would further enhance 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 purpose and outputs, the second gives a requirement, the third states cost. Information is front-loaded and easy to parse.

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 lookup tool with one fully documented parameter and clear annotations, the description covers all necessary context: purpose, inputs, outputs, requirements, and cost. No missing information is evident.

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 100% schema description coverage, the schema already fully documents the cust_id parameter. The description adds no extra parameter-level meaning beyond confirming the tool uses a customer ID. 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 looks up an iRacing driver profile by customer ID and lists returned data (iRating, license class, etc.). It distinguishes effectively from siblings like get_race_results and search_web, which serve different purposes.

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

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

The description specifies requirements (active iRacing subscription, $0.01 USDC per call), providing clear context for when to use the tool. It does not explicitly mention when not to use or compare with alternatives, but the sibling tools are sufficiently distinct that no further exclusion is necessary.

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 destructiveHint=false, so the description's addition of cost per call and the return format (subsession IDs, track info, etc.) provides useful behavioral context beyond the structured metadata. No contradictions 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 two sentences plus a requirement and cost line, all front-loaded and free of extraneous information. Every sentence adds value.

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

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

Given no output schema, the description adequately lists return fields. It covers the purpose, filters, cost, subscription requirement, and relationship to a sibling tool. Missing details like pagination or limits, but overall complete 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?

All four parameters have descriptions in the schema (100% coverage), so the description adds only a summary of filtering by date range, driver, or session name. This is helpful but does not significantly extend beyond the schema 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 clearly states the tool searches iRacing hosted race results by date range, driver, or session name, and lists the returned fields (subsession IDs, track info, winner, driver count). It distinguishes itself from the sibling tool 'get_race_results' by advising to use that for full results after obtaining subsession IDs.

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: requires an active iRacing subscription and costs $0.02 USDC per call. It also indicates that subsession IDs can be used with 'get_race_results' for full details. However, it does not explicitly state when not to use this tool or compare to other siblings like 'search_web'.

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, openWorldHint, and destructiveHint false. The description adds value by disclosing the cost per call ($0.01 USDC) and the dependency on SERPAPI_KEY env var, which are important operational details not covered by 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 with no filler. It front-loads the purpose and includes only essential details (cost, auth requirement, return type). 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?

For a simple tool with two parameters and no output schema, the description adequately conveys return type (organic results with rankings) and operational prerequisites. It doesn't detail error handling or pagination, but that's not critical for this simplicity.

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 q and limit are described in the schema). The description does not add extra meaning beyond what the schema provides, 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 states a specific verb ('Search the web') and resource ('via SerpAPI (Google search)'), and specifies it returns organic results with rankings. This clearly differentiates it from sibling tools that are racing-specific lookups.

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 context is clear: this is a general web search tool, contrasted with the racing-specific siblings. It doesn't explicitly state when not to use it, but the description and sibling names imply it's for generic queries. No exclusions are needed.

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