OpenAI Billing

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

Annotations provide idempotentHint=true and destructiveHint=false, and the description adds meaningful context beyond that: token config gives a permanent non-expiring connection, pasting a token gives session-only login, and calling with no args returns a login link. It does not describe errors or side effects, but it covers the important behavioral distinctions.

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

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

The description is front-loaded with the tool's purpose and uses three sentences to cover two authentication paths plus the no-args behavior. It is slightly dense and mixes configuration advice with invocation guidance, but every clause contributes 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 one-parameter auth tool with no output schema, the description covers the core workflows: browser login link, session token, and permanent header configuration. It does not mention response formats, error handling, or security warnings, but the essential selection and invocation information is present.

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

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

The input schema only shows a bare 'token' string with 0% description coverage. The description fully explains the parameter: it is an optional JWT for session login, and omitting it returns the login link. This completely compensates for the schema's lack of detail.

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 identifies the tool as authentication for MCP.AI IDE agents, using clear verbs like 'log in' and 'authenticate'. It distinguishes itself from sibling tools by scoping to the MCP.AI login flow and specifying two concrete authentication modes.

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

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

It gives clear context for when to use the tool (to authenticate MCP.AI for IDE agents) and provides explicit guidance for permanent vs session-only authentication. It does not explicitly name alternative tools or state when not to use it, so it stops short of 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?

The description adds behavioral detail beyond the readOnly/idempotent annotations by explaining conditional outputs (authenticated vs. pending). It doesn't mention side effects, but the annotations and nature of the tool make it transparent enough.

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 succinct, using two sentences to convey the tool's purpose and conditional behavior. No unnecessary words or 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 absence of parameters and output schema, the description fully covers the tool's behavior under different connection states, providing sufficient context for an agent to use it appropriately.

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?

There are no parameters, so schema coverage is complete. The description adds nothing about parameters because none exist, which aligns with the baseline 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 function: returning connection status and URLs. It distinguishes itself from sibling tools like 'authenticate' by focusing on status rather than authentication 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 implies usage for checking connection status and outlines conditional outcomes based on state, but does not explicitly contrast with alternatives such as 'authenticate'. Still, the context is clear and helpful.

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

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

The description adds substantial behavioral context beyond the generic annotations (readOnlyHint=false, openWorldHint=true). It reveals that invoke runs tools without installation, returns a connect link when credentials are needed, returns a checkout link when payment fails, and that install persists the MCP. It also discloses permission requirements for writes, enriching the agent's understanding of side effects and prerequisites.

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 that, while informative, is overwrought. It covers many actions and edge cases but lacks clear visual structure (e.g., bullet points or sections). It is front-loaded with purpose and uses arrows to guide flow, but the overall length could be trimmed for easier scanning. It is not egregiously verbose given the tool's complexity, hence a middle score.

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

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

For a tool with 23 parameters and 14 actions, the description is remarkably complete. It explains the entire lifecycle: search→describe→invoke, the exception handling for auth and payment, the distinction between invoke and install, the prompt library sub-flow, and the required permissions. It also hints at return values (connect links, slugs). No output schema exists, but the description covers the essential behavioral expectations, making it sufficient for agent 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?

Schema description coverage is 0%, so the description must compensate. It explains the meaning of the primary 'action' parameter and related workflow fields (query, mcp_id, tool_id, arguments, prompt_slug, etc.), but leaves many parameters unexplained—such as 'limit', 'immediate', 'tier_slug', 'conversation', 'report_context', and others. The coverage is partial, giving a baseline of 3; it does not fully compensate for the 0% 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 opens with a crisp definition: "The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them." It then details the core flow and enumerates every action (search, describe, install, invoke, etc.), making the tool's purpose unmistakable and distinguishing it from sibling tools like authenticate or toolkit_info.

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

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

Explicit guidance is provided throughout: "prefer invoke for a single/occasional use" vs "Use install only to make an MCP PERMANENT in the active toolkit," "list_tools lists what is callable right now," and "Writes ... require workspace owner/admin." It also explains when to use invoke even when the MCP is not installed, covering the core decision tree.

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 is consistent with the readOnlyHint and idempotentHint annotations, and it mentions the output includes totals and daily costs, which adds some behavioral detail. However, it does not go beyond the annotations in explaining side effects, rate limits, or other behavioral nuances, so it only partially enhances 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 concise, consisting of two short sentences with no redundant wording. It is front-loaded with the primary verb 'Get' and immediately conveys the core purpose, making it 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?

The description, combined with the schema, is insufficient for full usage. The lack of parameter explanations and output schema means the agent cannot know how to set the parameters or interpret the returned costs beyond 'totals and daily costs.' This incomplete context would likely require additional probing or trial.

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 provides no descriptions for any of the 7 parameters, and the description does not compensate. It vaguely references 'date range' but does not explain the roles of start_time, end_time, days_back, limit, group_by, or bucket_width. This leaves the agent unable to correctly construct a request.

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

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

The description clearly states the tool retrieves normalized OpenAI organization costs for a date range, which is a specific verb-resource pairing. It distinguishes itself from sibling tools like 'openai_billing_usage' and 'openai_billing_list_accounts' by focusing on costs rather than usage or account listing.

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 any guidance on when to use this tool versus the sibling tools. It lacks explicit conditions or comparisons, such as 'use this instead of X when you need costs aggregated by day.' The high-level purpose is clear but not the selection criteria.

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 is consistent with annotations (readOnly, idempotent, non-destructive). It adds context by noting the connections are 'linked to this install', providing a scoping detail not present in annotations. No contradictions, and the read-only nature is clear from the verb 'List'.

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

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

The description is a single, clear, and concise sentence with no superfluous words. It efficiently conveys the core functionality without elaboration or 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 is adequate for a simple listing operation but lacks completeness: it does not explain what 'connections' entails, what the return format might be, or the role of the 'account' parameter. Given the absence of an output schema, the description leaves some ambiguity about the expected result, making it moderately 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 schema includes a single parameter 'account' with no description, and the tool description does not mention it at all. With 0% parameter coverage, the description fails to clarify the purpose or meaning of the parameter, leaving users to guess whether it filters or specifies the account.

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 function: listing OpenAI Billing admin API connections. It uses a specific verb ('List') and identifies the resource ('OpenAI Billing admin API connections'), distinguishing it from sibling tools like openai_billing_cost and openai_billing_usage.

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

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

The description implies usage when one needs to see billing admin connections, but it does not explicitly state when to use it versus alternatives (e.g., openai_billing_cost or openai_billing_usage). It lacks any context about when not to use it or prerequisites, making the guidance implicit rather than explicit.

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 disclose read-only, idempotent, and non-destructive behavior, so the description does not need to repeat safety. It adds some context by specifying 'normalized' usage and the list of categories, but does not address pagination, rate limits, or required authentication. With annotations covering the safety profile, a 3 is appropriate.

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 with no filler content. It is front-loaded with the verb and resource, making it easily scannable.

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 8 parameters and no output schema, the description is under-specified. It does not explain parameter semantics, return format, or any prerequisites like authentication. The high-level purpose is clear, but the tool's configuration and output are not sufficiently described for the agent to invoke it effectively.

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

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

Schema description coverage is 0%, and the description only indirectly hints at the 'kind' parameter by listing usage categories. It provides no meaning for limit, time filters (start_time, end_time, days_back), group_by, account, or bucket_width, leaving the agent without sufficient context to set these parameters correctly.

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 action ('Get') and the resource ('normalized OpenAI organization usage'), and enumerates the specific usage categories (completions, embeddings, etc.). This distinguishes it from sibling tools like openai_billing_cost, which focuses on cost, and openai_billing_list_accounts.

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 retrieving usage data but provides no explicit guidance on when to choose this tool over alternatives such as openai_billing_cost. There is no 'use this when' or 'instead of' statement, leaving the agent to infer from the name and resource.

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 include 'idempotentHint': true and 'readOnlyHint': false, suggesting the tool may have effects but is safe to retry. The description adds that the conversation array is needed for reproduction, but does not clarify whether retries cause duplicate reports, which is a behavioral gap.

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 concise (two sentences) and front-loads the primary purpose. It includes a practical instruction about the conversation array, but could be slightly more structured if it separated the purpose from usage 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?

Given the tool has 3 parameters, no output schema, and no schema coverage, the description only partially explains what to provide. It mentions the conversation array but omits any details about what the message and context parameters are for, or what happens after reporting, leaving it incomplete 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?

Schema description coverage is 0%, and the description only mentions 'conversation' as the array to include, but does not explain the purpose of 'message' or 'context' beyond what the schema names imply. It fails to compensate for the lack of schema documentation.

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

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

The description clearly states the tool's purpose: reporting bugs, missing features, or feedback. It uses specific verbs and resources, but does not explicitly distinguish from sibling tools, although none of the siblings seem to handle bug reporting.

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 this tool (for reporting issues or feedback) but provides no explicit guidance on when not to use it or alternatives. The inclusion of 'Include the conversation array...' gives some context, but not a clear usage policy.

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. The description adds the specific detail that it shows both platform and adapter versions, which is helpful 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?

A single, front-loaded sentence with zero waste. Every word contributes to the meaning.

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 informational tool with no parameters and no output schema, the description sufficiently conveys what it provides. It could mention the output format, but for version info this is typically understood.

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

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

The tool has zero parameters, and schema coverage is trivially 100%. The description does not need to explain parameters; the baseline score of 4 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 explicitly states it 'Show[s] the current MCP platform and adapter versions' with a clear verb and resource. It is distinct from sibling tools like authenticate or connect, which serve completely 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 purpose is unambiguous and the context makes it obvious when to use it (whenever version info is needed). No alternatives exist among siblings, so explicit exclusions are unnecessary, though not 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?

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, which cover the safety profile. The description adds that it returns current state but does not disclose any additional behaviors (e.g., performance, data freshness, or any side effects). It is consistent with annotations, so no contradiction, but adds minimal extra 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 a single sentence that is entirely informative with no filler. It is front-loaded with the main action and lists the key components. It is appropriately concise for a simple informational 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 has no parameters, no output schema, and is a simple read-only info tool, the description adequately lists what it returns (installed MCPs, connection status, accounts, catalog tools count). It could go further by specifying the structure of the return value (e.g., JSON object shape), but for its simplicity, it is close to complete. Slight deduction for not describing the exact format.

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?

This tool has zero parameters, and the schema coverage is 100% (trivially). The description does not need to explain any parameter semantics since there are none. The description itself implies no inputs are needed, and the zero-parameter baseline is 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 returns the current toolkit state, enumerating specific elements (installed MCPs, connection status, accounts, catalog tool count). The verb 'Returns' with 'toolkit state' is specific and distinguishes it from sibling tools like 'connect' or 'authenticate', which are action-oriented.

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 explains what the tool does but does not provide any guidance on when to use it, such as typical use cases or when to prefer it over other tools. It is an informational read-only tool, so it's likely obvious, but explicit context is missing.

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