AINumbers Fintech Intelligence Suite

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 adds value by explaining that it is browser-based, client-side only, and involves simulation with zero network calls, reinforcing the non-destructive, read-only nature.

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 effectively front-load the main purpose and then provide technical context. Every phrase is informative, though the second sentence is dense with details like 'AIN Bridge' and 'AINumbers tool' that might require familiarity.

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 lack of output schema, the description adequately covers inputs, processing, output (Policy Mandate), and security characteristics (zero PII, zero network). It doesn't explain error handling or edge cases, but overall is sufficient for a sandbox 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 single parameter 'inputs' is an object with 100% schema coverage, and the description adds semantic meaning by enumerating what can be set (spend caps, MCC allowlists, etc.), which goes beyond the schema 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 simulates agent payment policies for tokenized A2A corridors, listing specific configurable elements (spend caps, MCC allowlists, etc.) and distinguishing it from sibling tools like ap2_aml_mandate_builder by emphasizing simulation and client-side execution.

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 does not explicitly state when to use this tool versus alternatives like build_google_ap2_mandate or lint_mcp_tool_definition. It mentions browser-based and zero PII but lacks comparative 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, indicating a safe read operation. The description adds key behavioral context: 'Browser-based, client-side only. Zero PII. Zero network.' This confirms no data leaves the browser and no server calls are made, which is valuable beyond the annotations. No 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?

The description is multiple sentences with some redundancy (e.g., 'zero PII' mentioned twice). While it covers purpose, technical details, and a link, the first sentence 'Anchor agentic tool for Cat-12' is vague and not front-loaded. Overall, it is adequate but not tightly structured; every sentence contributes, but rephrasing could improve clarity.

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, no output schema) and strong annotations, the description provides sufficient context: it explains the translation process, client-side execution, and a link for interactive use. Lacking output schema, it still hints at the output being a structured JSON. The description is complete for an agent to understand what the tool does and how it behaves, though details on output format could strengthen it.

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 a description for the 'inputs' parameter stating it is a map of element IDs to values. The description adds that inputs are 'applied via the AIN Bridge prefill', providing minimal extra context. Since the schema already explains the parameter well, the description adds limited value, justifying a baseline score of 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?

The description clearly states the tool's purpose: translating AML/BSA program controls, TM rules, and customer risk policy into a structured Policy Mandate JSON. The verb 'translate' and resource 'controls into JSON' are specific. However, it does not explicitly differentiate from siblings like 'build_google_ap2_mandate', though the mention of 'AP2 AML' provides some distinction. A score of 4 reflects clear purpose without sibling differentiation.

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 does not provide explicit guidance on when to use this tool versus alternatives. It calls the tool an 'anchor agentic tool for Cat-12' which vaguely implies primary usage but lacks direct comparison with siblings like 'agentic_mandate_sandbox' or 'build_google_ap2_mandate'. No when-not-to-use or prerequisites are stated.

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 readOnly, idempotent, and non-destructive. The description adds crucial behavioral traits: renders an interactive widget, inputs via AIN Bridge, runs client-side with zero PII and zero network. This goes well beyond annotations and fully informs the agent.

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 adding unique value: purpose+checks, usage context, and behavioral details. No filler or redundancy; information is front-loaded with the main 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 lack of an output schema, the description could explicitly mention the output (e.g., audit report or widget display). However, it implies the output is the widget itself and describes inputs and behavior well, so it is mostly 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% for the single 'inputs' parameter, so baseline is 3. The description adds value by explaining that inputs are applied via AIN Bridge prefill and that the tool renders a widget, giving context on how the parameter is used.

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 audits MCP OAuth 2.1 authorization and specifies exact RFC checks (9728, 8707) and risk assessments. It distinguishes from sibling tools by focusing on OAuth rather than general sandbox, mandate, or validation tasks.

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 when a developer is securing an MCP server's authorization,' providing clear usage context. While it does not mention alternatives or when not to use, the context is sufficient for an agent to decide.

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 provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: 'Browser-based, client-side only, zero PII', 'inputs are applied via the AIN Bridge and the tool runs client-side (zero PII, zero network)'. This goes beyond annotations by explaining execution environment and data handling, though it does not detail all edge cases.

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. The first sentence is concise and informative. The second sentence repeats 'zero PII' and describes the widget rendering, which could be more concise or merged. It is not overly long but has some 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?

Given the tool has one nested parameter and no output schema, the description covers inputs (weight matrix applied via AIN Bridge), outputs (comparison matrix and memo), and behavioral traits (client-side, no PII). It provides a good overview for an interactive widget, though details on output structure or error handling are 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?

Schema coverage is 100% with one parameter 'inputs' described as a map of IDs to values. The description mentions 'user-adjustable 1-5 weighting matrix' and 'applied via AIN Bridge prefill', adding some meaning beyond the schema. However, it does not detail the structure or valid values for the input map, leaving some 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 the tool's purpose: 'Score and compare BaaS providers across 10 capability dimensions'. It specifies the verb 'score and compare', the resource 'BaaS providers', and the output 'weighted comparison matrix and Markdown evaluation memo'. There is no ambiguity, and it differentiates from sibling tools due to its unique focus on BaaS provider evaluation.

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 context but does not explicitly state when to use this tool versus alternatives. It mentions the capability dimensions and outputs, but lacks guidance on prerequisites or when not to use it. Given the sibling tools are diverse, some explicit differentiation would help, but it's adequate.

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 readOnlyHint=true and idempotentHint=true. The description adds behavioral context: 'renders the interactive AINumbers tool as a widget; inputs are applied via the AIN Bridge and the tool runs client-side (zero PII, zero network).' This discloses execution model and data handling beyond 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 extremely concise, consisting of two sentences that cover purpose, target specification, and operational context with no redundancy or filler 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 has one parameter, no output schema, and clear annotations, the description covers key aspects: what it builds, how it runs (client-side, zero PII, zero network), and its target spec. Minor context missing (e.g., what 'VDC' stands for) but acceptable for domain-specific tools.

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 the single 'inputs' parameter well-documented. The description adds value by explaining that inputs are applied via AIN Bridge prefill and that the tool renders as a widget, providing context on how parameters are used.

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 explicitly states the tool's function: 'Build or validate a Google AP2 Checkout/Payment Mandate VDC (Open/Closed).' It differentiates itself by noting it targets the external AP2 spec, not the AINumbers Policy Mandate, distinguishing it from siblings like 'ap2_aml_mandate_builder' and 'validate_ap2_mcp_policy'.

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 context by specifying the tool targets the external AP2 spec, implying it is not for AINumbers Policy Mandate usage. However, it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria, leaving a slight gap.

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 mark it as read-only, idempotent, and non-destructive. The description adds that the tool runs client-side with zero PII and zero network, providing valuable 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 two sentences, front-loading the core purpose and then adding usage guidance and behavioral notes. Every sentence adds value with no 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?

Given the complexity of comparing multiple protocols and the lack of an output schema, the description mentions rendering an interactive widget, which implies the output format. However, it does not detail the return value structure, which is acceptable for a tool that generates a visual widget.

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, describing the 'inputs' parameter as a map of element IDs to values. The description adds clarity by stating it is applied via AIN Bridge prefill, which helps the agent understand how to construct the input.

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 explicitly states the tool compares agentic payment protocols (AP2, ACP/Shared Payment Token, x402, Visa TAP, Mastercard Agent Pay) across multiple dimensions, which clearly defines its purpose and distinguishes it from sibling tools that focus on individual protocols or other tasks.

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 a clear use case: 'Use when a developer or strategist needs to orient across the fragmenting agentic-payments standards.' It does not explicitly state when not to use or list alternatives, but the context is sufficient for the agent to decide.

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, idempotentHint, and destructiveHint false. The description adds significant value by stating 'Browser-based, client-side only', 'Zero PII', 'zero network', and 'Renders the interactive AINumbers tool as a widget', which provides crucial behavioral and privacy context beyond the annotations. No 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?

The description is four sentences, front-loading the core purpose. Each sentence contributes useful information, but the mention of the interactive link and client-side behavior could be condensed slightly without losing meaning. Overall, it is efficient and well-structured.

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

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

Given the complexity of a KYC risk scoring tool with no output schema, the description lacks details about what the tool returns or produces. It states it 'Renders the interactive AINumbers tool as a widget', but does not clarify if the output is a visual score or data. An agent may be uncertain about the expected result. The absence of output schema underscores the need for clearer return value explanation.

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% for the single 'inputs' parameter, which is described as a map of input element IDs to values. The description adds meaning by explaining that inputs are applied via the AIN Bridge and used to prefill the interactive widget, enhancing understanding beyond the schema. However, it does not detail the specific input elements or their types, leaving some 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 it scores KYC risk across six FATF dimensions, but does not explicitly differentiate from sibling tools like ap2_aml_mandate_builder or baas_provider_comparator, which also deal with AML. The verb 'score' is specific and the resource 'KYC risk' is clear, but more precise differentiation would improve clarity.

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 scoring KYC risk client-side, but lacks explicit guidance on when to use this tool vs alternatives (e.g., other AML tools) or when not to use it. The mention of linking to an interactive page suggests a specific use case, but no exclusions or alternatives 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context beyond annotations: 'Renders the interactive AINumbers tool as a widget; inputs are applied via the AIN Bridge and the tool runs client-side (zero PII, zero network).' 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?

Two sentences with no wasted words. First sentence covers the three core actions, second sentence provides usage context and key behavioral traits. Front-loaded with the most important 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 tool with three actions, nested parameter, and no output schema, the description covers purpose, usage, and critical behavioral information (client-side, no network, no PII). It could be more explicit about return values or output format, but given the annotations and schema, it is mostly 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 input schema has one parameter 'inputs' with a description referencing a manifest input_schema. Schema description coverage is 100% but the description is generic and external. The description adds 'inputs are applied via the AIN Bridge prefill' but does not elaborate on structure. This meets the baseline for high schema coverage but could be more specific.

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 three specific actions: decoding a header, linting a payload, and describing the 402 flow. It uses specific verbs and resource types, and distinguishes from sibling tools like 'lint_mcp_tool_definition' and 'validate_a2a_agent_card' by focusing on x402 payment integration.

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 when a developer is integrating x402 and needs to inspect a header, check a payload shape, or understand the flow.' This provides clear usage context. However, it does not mention alternative tools for similar tasks or explicitly state when not to use it.

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, idempotent, non-destructive hints. The description adds valuable context: runs client-side, zero PII, zero network, and inputs applied via AIN Bridge. This goes beyond annotations by explaining privacy and execution model.

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 fluff: first states purpose, second explains mechanism and privacy. 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?

Despite the tool's complexity (inspecting signatures, scoring readiness, rendering a widget), the description lacks details on output format, scoring criteria, or what the interactive widget displays. No output schema is provided, so description should compensate.

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% for the single parameter 'inputs', providing a baseline. The tool description merely reiterates that inputs are applied via AIN Bridge, adding no extra semantics beyond what the schema already provides.

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

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

The tool clearly states it inspects a Visa Trusted Agent Protocol HTTP Message Signature and scores TAP readiness. It differentiates from siblings like 'score_mcp_readiness' by specifying the protocol and the use of AINumbers. However, the exact inspection process is vague.

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 explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, conditions, or scenarios where this tool is appropriate, leaving the agent to infer from the name and 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?

Annotations already provide readOnlyHint, idempotentHint, and non-destructive nature. The description adds key behavioral details beyond annotations: runs client-side (zero PII, zero network), renders a widget, and applies inputs via AIN Bridge. 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?

Two sentences: the first captures purpose and output, the second adds usage context and technical details. No fluff, 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?

Tool has single parameter with full schema coverage, clear annotations, and no output schema. The description adequately covers output (returns findings, conformance score, recommended annotation set), making it complete for an agent without needing output 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?

Schema coverage is 100% with one parameter 'inputs' described as an object. The description adds meaning: 'Map of tool input element IDs to values (see manifest input_schema). Applied via AIN Bridge prefill.' This clarifies the parameter's role beyond the schema's generic description.

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 specifies the verb 'Validate', the resource 'MCP tool definition', and the scope (JSON Schema 2020-12, naming, output-schema, annotation rules). It distinguishes itself from siblings by being a linter for tool definitions, not for server JSON or other aspects.

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 use: 'Use when a developer wants to check an MCP tool definition before publishing.' Does not explicitly state when not to use, but context from sibling tools implies alternatives exist for other validations. The mention of client-side and zero PII also guides usage.

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 confirm read-only and idempotent behavior. The description adds value by specifying that results include deep-links and describing the prefill invocation mechanism for certain tools (e.g., '#in=...'), which goes 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 brief (two sentences) and front-loads the main purpose. However, the second sentence includes a technical detail about prefill encoding that may be dense for some agents, slightly reducing 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 lack of output schema, the description covers the return type (deep-links) and a special invocation feature. However, it omits information about pagination, error handling, or how the parameters affect results, leaving gaps 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?

The input schema has three parameters (limit, query, category) with 0% coverage. The description does not explain the purpose or expected values of any parameters, leaving the agent without guidance on how to populate them.

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 tool as searching the AINumbers catalog with a specific count (420+ tools) and specifies the return format (deep-links). It distinguishes from sibling tools by focusing on a catalog search, which is unique among sibling 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 provides no guidance on when to use this tool versus alternatives, nor does it mention when not to use it. It only describes functionality without contextual usage 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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds significant behavioral context: client-side execution, zero PII, zero network, rendering an interactive widget, and returning risk scores. This exceeds 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?

Two sentences of well-structured, front-loaded content. Every sentence adds value 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?

The description covers purpose, behavior, and basic outputs (risk score, patterns) but lacks detail on result format or structure. Given the tool's simplicity and lack of output schema, it is adequate but could be more explicit.

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 a clear description of the input parameter. The description adds that inputs are applied via AIN Bridge prefill, but overall meaning is already captured by the schema. 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 begins with a clear action verb 'Scan', specifies the resource 'MCP tool description/manifest', and defines outputs 'risk score and flagged patterns'. This distinctly differentiates it from sibling tools like 'lint_mcp_tool_definition'.

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 the tool is for security scanning but does not explicitly state when to use it versus alternatives. It mentions zero network and client-side execution, which helps but lacks direct guidance on 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?

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: renders an interactive widget, runs client-side with zero PII and zero network, which goes beyond annotations and helps the agent understand safety and execution model. No 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?

Two sentences: first states purpose, second details execution context. No extraneous words or repetition. Efficient 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 complexity (nested object parameter, no output schema), the description explains execution (client-side, zero network) but lacks return-value details. The agent might need to know what the score output looks like or how to interpret the widget. Adequate but not fully 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 a single parameter 'inputs' described as a map applied via AIN Bridge prefill. The description repeats this ('inputs are applied via the AIN Bridge') without adding new meaning. Baseline 3 is appropriate as schema already documents the parameter adequately.

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 computes a composite MCP server ship-readiness score across specific categories (tool definitions, server.json, OAuth, etc.), which distinguishes it from sibling tools like 'lint_mcp_tool_definition' and 'scan_tool_poisoning' that focus on individual aspects.

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 assessing overall readiness (e.g., 'ship-readiness score'), but does not explicitly state when to use this tool versus alternatives like 'audit_mcp_oauth' for OAuth-specific checks or 'lint_mcp_tool_definition' for definition quality. No when-not-to-use guidance is 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds valuable context: runs client-side, zero PII, zero network, uses AIN Bridge prefill, and renders a widget. This goes beyond annotations in describing execution environment and data safety.

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 efficient sentences: first sentence captures core purpose, second adds key technical context (widget, client-side, zero network/PII). No redundancy, front-loaded, 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?

The description lacks any mention of what the tool returns (e.g., validation results, errors). Without output schema, this is a gap. It mentions rendering a widget but not the result format. Otherwise, context is adequate for a validation 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 a clear description of the 'inputs' parameter. The description reinforces that inputs are applied via AIN Bridge but adds no new semantic detail beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states it validates an A2A agent-card.json against v1.0 shape, checks signatures, and confirms extension declarations. It also mentions rendering an interactive widget and running client-side, distinguishing it from sibling tools like lint_mcp_tool_definition or validate_mcp_server_json.

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 A2A agent card validation but provides no explicit guidance on when to use this tool versus alternatives, nor excludes scenarios. The sibling context helps but the description itself lacks explicit when-to-use 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?

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds rich behavioral context: 'auto-generates,' 'simulates,' 'renders the interactive AINumbers tool as a widget,' 'inputs are applied via the AIN Bridge,' and 'tool runs client-side (zero PII, zero network).' This goes beyond annotations and fully informs the agent.

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, each adding distinct value: validation purpose, auto-generation and simulation, usage guidance, and behavioral details. It is efficient and front-loaded with the core function.

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 complexity (validation, auto-generation, simulation, widget rendering) and lack of output schema, the description covers most aspects but does not explain what the tool returns (e.g., validation results or generated definitions). This minor gap prevents a 5.

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 defines one parameter 'inputs' with a description. The tool's description adds meaning by explaining that inputs are applied via AIN Bridge prefill and that the tool renders the AINumbers widget. Since schema coverage is 100%, the baseline is 3, but the description provides additional context, earning a 4.

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 validates AP2 Policy Mandate JSON payloads against a specific schema, auto-generates MCP tool definitions, and simulates agent ingestion. The verb 'validate' with the resource 'AP2 Policy Mandate JSON payloads' is specific and differentiates it from siblings like 'validate_mcp_server_json' or 'audit_mcp_oauth'.

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 when authoring or testing AP2 agentic payment policies,' providing clear context. It also describes how inputs are applied via AIN Bridge and that the tool runs client-side, but does not explicitly mention when not to use it or name alternative 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, destructiveHint=false), the description reveals that the tool runs client-side with zero PII and zero network, and renders an interactive widget via AIN Bridge. This adds valuable behavioral context.

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 unnecessary words. The first sentence front-loads the primary purpose and outputs, followed by usage guidance and additional behavioral context. 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?

The description adequately covers the tool's purpose, outputs (findings, score, optional skeleton), usage scenario, and runtime behavior (client-side, zero PII). Despite no output schema, the description covers key outputs.

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 describes the 'inputs' parameter. The description adds marginal value by mentioning that inputs are applied via the AIN Bridge, but this is already implied in the schema. Hence, the description does not significantly enhance parameter understanding 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 validates an MCP server.json against a specific schema and registry publishing rules, returning findings, a readiness score, and an optional skeleton. It distinguishes itself from siblings like 'lint_mcp_tool_definition' by focusing on server.json validation and registry readiness.

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 'use when a developer wants to check a server.json before publishing to the MCP Registry,' providing clear context. However, it does not explicitly mention when not to use the tool or suggest alternatives like 'lint_mcp_tool_definition' for linting tool definitions.

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