AWS Billing

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

Beyond the annotations, the description reveals important behavior: no args returns a login link, passing a token creates a session-only login, and config-header setup provides a permanent non-expiring connection. It clearly sets expectations about the two modes and their persistence. It does not contradict 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 and packs in the main modes and call forms without wasted words. The use of 'Best:' and 'Or' helps structure the two options. It is slightly dense but remains readable and appropriately sized.

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

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

For a simple one-parameter tool with no output schema, the description covers how to invoke it and what each invocation mode accomplishes. It does not discuss invalid tokens or error handling, but those are not essential for basic selection and invocation. Overall, it is complete enough for an agent to use.

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 only declares an optional string `token` with no description, so the description must carry the meaning. It explains that `token` is the pasted JWT access token and that omitting it yields a login link. This adds meaningful semantics beyond the bare 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 identifies the tool as authentication for MCP.AI in IDE agents, explaining browser login and access token handling. It gives specific actions (log in, copy token, pass token, or call with no args to get a link) rather than just repeating the name. It does not explicitly differentiate from the sibling 'connect' tool, so it stops short of a 5.

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

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

The description gives concrete usage instructions: add the token as an Authorization header for a permanent connection, or paste it for session-only login. It also specifies call shapes: `{ token: "<jwt>" }` or no arguments to get the link. It lacks explicit when-not-to-use guidance relative to sibling tools, so it is not a 5.

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

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

Annotations already indicate a safe read operation. The description adds that the adapter drains NextPageToken internally, which is a valuable behavioral detail about pagination not captured in 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 clearly states the action, the second adds a pagination note. 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?

The description covers the core function and pagination, and annotations handle safety, but it lacks parameter details and return information. Given the simple tool, it is fairly 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 0%, and the description only hints at date range via 'for a date range,' not explaining limit, account, or specific date formats. It does not compensate for the lack of 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 'Get normalized AWS Cost Anomaly Detection anomalies' with a specific verb and resource, and the date range scope distinguishes it from cost and forecast siblings.

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 anomaly detection but does not explicitly compare with siblings or provide when/when-not guidance. The date range mention gives some context but no 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?

The description adds one behavioral detail: the adapter drains NextPageToken internally, handling pagination automatically. This is useful beyond the annotations, which already declare read-only and idempotent. However, the mention of 'daily breakdown' could mislead since granularity can be monthly or hourly per the schema, and no other behavioral traits (e.g., rate limits, authentication) are disclosed.

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 very concise, consisting of two sentences. It front-loads the core purpose and adds only one extra detail (pagination). There is no wasted wording, and it is well-structured for quick comprehension.

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 being a read-only tool with annotations, the description is insufficient for a tool with 6 parameters, no output schema, and zero parameter documentation. It does not explain the expected return format, date handling, or how 'normalized' data is structured. The pagination note is helpful but does not address the overall lack of context needed for effective use.

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 parameter descriptions (0% coverage), and the description does not compensate. It fails to explain the meaning or format of account, start_date, end_date, days_back, group_by, or granularity. The high-level mention of 'daily breakdown' and 'top services' does not map to parameters, leaving the agent without essential information to construct valid requests.

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 normalized AWS Cost Explorer cost and usage data with daily breakdown and top services. It identifies the specific resource (Cost Explorer) and the operation (get), making the tool's purpose unambiguous and differentiated from sibling tools like anomaly detection or forecasting.

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 such as aws_billing_anomalies or aws_billing_forecast. The description does not mention any exclusions, prerequisites, or preferred contexts, leaving the agent to infer usage without explicit direction.

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 the tool as readOnlyHint: true, idempotentHint: true, and destructiveHint: false, so the safety profile is known. The description adds context about the date range scoping ('next 30 days or an explicit date range') but does not disclose other behavioral traits like pagination, rate limits, or return format. With annotations covering the core safety aspects, the description provides some additional value but not extensive 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 a single, concise sentence that immediately states the core functionality. It is front-loaded with the verb and resource, and every word adds value. There is no repetition of schema or annotation information, and no fluff. Perfectly 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?

The tool has 5 parameters, 0% schema coverage, and no output schema. The description only mentions the date range concept but does not explain the metric options, granularity, account filtering, or expected output. Given the complexity of an AWS forecasting tool, this description is inadequate for an agent to correctly select and invoke the tool without external knowledge.

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 5 parameters (metric, account, end_date, start_date, granularity). The description only hints at date range via 'next 30 days or an explicit date range', but does not specify the parameter names or their formats. The metric and granularity enums are entirely undocumented. Given the low coverage, the description fails to compensate for the missing parameter semantics.

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 normalized AWS Cost Explorer forecast for the next 30 days or an explicit date range.' It identifies a specific verb ('Get'), a concrete resource ('normalized AWS Cost Explorer forecast'), and a scope ('next 30 days or explicit date range'). This differentiates it from sibling tools like aws_billing_cost (which likely returns historical costs) and aws_billing_anomalies (which deals with anomalies).

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 when to use the tool (for forecasting future costs) but does not explicitly state when not to use it or mention alternatives. It does not reference sibling tools like 'aws_billing_cost' for historical data. Clear context is provided, but exclusions and alternative tool guidance are missing.

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

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description adds little behavioral context. It does not disclose what a 'credential record' contains, whether account filtering affects output, or any other operational details.

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 succinct sentence with the verb first and no filler words. It is concise while still conveying the core 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?

For a simple read-only listing tool with one optional parameter, the description covers the primary purpose, but it leaves gaps around the account parameter and does not connect usage context to sibling billing tools. It is minimally 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 description coverage is 0% and the description never mentions the optional 'account' parameter, leaving its purpose and filtering behavior unexplained. The schema only provides its type as 'string', so the agent gets no guidance on how to use it.

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

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

The description uses the specific verb 'List' and names the exact resource ('AWS Billing credential records linked to this install'), making the tool's purpose clear. It also distinguishes itself from sibling cost/forecast/anomaly tools by focusing on credential records.

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 use when the agent needs to see billing credential records, but it does not explicitly state when to use this tool versus alternatives like aws_billing_cost or aws_billing_anomalies. No exclusions or alternative 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 readOnly, idempotent, and non-destructive. The description adds concrete output behavior (authenticated:true vs connect_url) beyond annotations, which is valuable context. No contradiction.

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, front-loaded with the core purpose, and each sentence adds necessary detail about behavior states. No redundancy or filler.

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

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

Given the tool's simplicity (0 params, no output schema), the description fully covers expected return scenarios and conditions. It sufficiently explains the tool's functionality for an agent to invoke it correctly.

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

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

Tool has zero parameters, so schema coverage is trivially 100%. Description adds meaning about the connection states and returned URLs, which covers the tool's behavior adequately. Baseline 4 is appropriate since no params to document.

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 the tool returns connection status and URLs, with specific conditions for authenticated state and missing credentials. Distinguishes from siblings like authenticate by focusing on status rather than establishment.

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?

Does not explicitly state when to use or when not to use. Context implies it's for checking connectivity, but no alternatives or exclusions are mentioned. The conditional behaviors describe outputs, not usage scenarios.

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 discloses that invoke runs tools without installing, returns connect/checkout links when credentials or payment are needed, and that writes require admin. Annotations only say readOnlyHint=false and destructiveHint=false, and the description adds substantial behavioral context 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 a single dense paragraph covering 14 actions and the prompt library. It is long but every sentence adds unique information, and it front-loads the core purpose. However, it would benefit from bullet points or logical grouping to improve scanability for such a multi-faceted tool.

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

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

Given the tool's complexity and lack of output schema, the description covers the core workflow, behavioral side effects, permission requirements, the prompt library, and links for auth/payment. It explains what describe returns, what invoke returns, and how list_tools works, providing sufficient context for an agent to select and invoke with confidence.

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 0% schema description coverage across 23 parameters, the description must compensate. It mentions key parameters like action, mcp_id, tool_id, arguments, and prompt-related fields in context, but does not explicitly describe all parameters (e.g., limit, query, immediate, tier_slug). The core flow explanation helps infer usage, but full parameter semantics are lacking.

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 the mcp.ai marketplace catalog and execution platform, listing concrete verbs and resources (search, describe, invoke, install) and distinguishing it from sibling tools like aws_billing and authenticate. It explains the core flow and differentiates from the prompt library.

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 explains when to use each action: search to discover, describe for profiles, invoke for one-off runs, install for permanent additions, subscribe/cancel for billing, request_mcp for new capabilities. Also gives exclusions (prefer invoke over install for occasional use) and permission requirements (writes need owner/admin).

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 idempotentHint=true and destructiveHint=false, covering safety aspects. The description adds behavioral context by instructing to include conversation for reproduction, which hints at expected usage but does not disclose side effects or response behavior. It neither contradicts nor enriches annotation info significantly, so it meets the baseline.

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—two short sentences that front-load the purpose and add a practical instruction. Every word earns its place, with no redundant phrasing or unnecessary details.

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 only 3 parameters but 0% schema coverage, the description falls short. It does not explain what the tool returns, does not describe the message or context parameters, and lacks any mention of prerequisites or side effects. For a tool that is primarily for user feedback, this is insufficient for an agent to invoke it correctly without additional assumptions.

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. It only mentions the 'conversation' parameter explicitly ('Include the conversation array'), while the required 'message' parameter and optional 'context' are not described. This leaves the agent guessing about the semantics of other parameters, especially since the schema itself provides no 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's purpose with a specific verb ('Report') and resource ('bug, missing feature, or send feedback'), making it distinct from sibling tools like billing or authentication. It also adds a key detail about including conversation data for reproduction, which helps differentiate its use case.

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 when to use the tool (when reporting issues or feedback) but does not explicitly state when not to use it or list alternatives. However, given the sibling set, the context is clear enough that no other tool would handle bug reports, so it is 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it's a safe, non-mutating operation. The description adds that it covers both MCP platform and adapter versions, which is useful context beyond the annotations. No contradiction.

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 one concise sentence that directly states the tool's function with no unnecessary words. It is appropriately 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 there are no parameters and the tool is simple, the description is complete enough. The annotations cover safety profile, and the output schema is absent, but for a version display tool, it's adequate. It could mention the return format, but it's not critical.

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

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

The tool has zero parameters, so there is no schema to document. The description appropriately explains what the version information covers, which is sufficient given there are no parameters to explain.

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 shows current MCP platform and adapter versions, which is specific and distinguishes it from sibling tools like 'toolkit_info' which likely provides broader information. However, it does not explicitly mention that it returns version details for both platform and adapters, but the purpose is clear.

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 it should be used when users want to know the versions, but it does not provide explicit context for when to use it versus other tools. Sibling tools like 'toolkit_info' might also provide version info, but there's no guidance on when to choose this one.

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, destructiveHint=false, and idempotentHint=true, so safety is covered. The description adds value by detailing the content of the returned state (installed MCPs, connection status, accounts, tool counts), which goes beyond annotations and gives the agent a clear picture of the output.

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

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

The description is a single, well-structured sentence that front-loads the purpose and lists the specific elements returned. There is no redundant or fluff content.

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

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

Given the tool's low complexity (no parameters, no output schema), the description adequately compensates for the missing output schema by itemizing the returned information. It tells the agent what to expect without being overly verbose. A slight omission is the format/structure of the response, but this is not critical for a status 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?

With zero parameters, the schema coverage is trivially 100%. Per the baseline guidance for 0-param tools, a score of 4 is appropriate. The description doesn't need to explain parameters since none exist, and it provides sufficient context about 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 states a specific action: 'Returns the current toolkit state' and lists concrete items (installed MCPs, connection status, accounts, catalog tool counts). This clearly differentiates it from siblings like show_version (version) or connect/authenticate (actions).

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 tells what the tool does but does not explicitly state when to use it vs. alternatives. It implies a status-checking purpose but offers no exclusions or comparative guidance among sibling tools like connect or show_version.

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