SINTEGRA: SUFRAMA

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

Beyond the annotations (readOnlyHint=false, idempotentHint=true), the description discloses important behavioral traits: the login happens in a browser, the result is an access token, and there are two distinct persistence modes (permanent non-expiring vs session-only). It also notes that passing no arguments returns a link. This adds meaningful context about the workflow and side effects, though it does not mention what happens to existing sessions or token storage.

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 compact and front-loaded, starting with the core purpose, then providing two usage alternatives. Every sentence adds information: purpose, recommended config approach, and session-only fallback with parameter usage. No fluff or repetition, and the structure flows logically from permanent to session-based authentication.

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 (two auth modes, browser interaction, optional token parameter), the description covers the essential scenarios and how to invoke them. It even notes the no-argument behavior. However, with no output schema and no mention of return values beyond 'get the link', the agent may not know what the response contains (e.g., a URL vs confirmation). Still, for a simple auth tool, the description is largely adequate.

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, the description compensates well. It explains the 'token' parameter's semantics: a JWT to be pasted for session-only login, and that omitting the token triggers a link generation flow. It also clarifies the usage context ('after the user pastes'), which gives the agent concrete invocation guidance. The only gap is that it doesn't specify the exact expected input format beyond 'jwt' or any validation details.

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: logging in and obtaining an access token for MCP.AI. It specifies the concrete actions (log in in the browser, copy the access token) and distinguishes itself from siblings like 'connect' by focusing on authentication/token acquisition. The verb 'log in' and resource 'access token' make the function unambiguous.

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

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

The description provides clear usage context: it explains two distinct invocation modes (permanent via config header, session-only via token argument) and explicitly recommends the config option as 'Best'. It also clarifies when to call with no arguments ('to get the link'). However, it does not explicitly contrast with sibling tools like 'connect', so the when-not-to-use guidance is 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds behavioral detail about the response structure (authenticated:true and empty pending[] vs. connect_url) but does not disclose potential side effects or rate limits, which is acceptable given 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-loaded with the core purpose, and every sentence adds value by explaining the two possible response states. 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?

Given the tool has no parameters, no output schema, and clear annotations, the description is complete enough. It explains the key output variations (authenticated vs. connect_url) and mentions per-install URLs, which is sufficient for a status-check 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 tool has zero parameters, so the description does not need to explain parameter semantics. The baseline for 0 params is 4, and the description appropriately focuses on the output behavior instead.

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 connection status and URLs, and explains the two possible states (all connected vs. missing credentials). It distinguishes from siblings like authenticate and toolkit_info by focusing on status/URLs rather than actions or 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?

The description implies when to use it (to check connection status or get connect URLs when credentials are missing) but does not explicitly state when not to use it or mention alternatives. It provides context but lacks explicit 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 provide only readOnlyHint=false and destructiveHint=false, but the description adds deep behavioral detail: invoke works even when an MCP is not installed, returns a connect link for missing credentials, returns a checkout link for empty wallets, and writes require workspace owner/admin. It also clarifies that search/describe flag installed vs. available status. This far exceeds the annotations' disclosure.

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 information-dense and every sentence adds value, but it is one very long paragraph that is hard to scan. It would benefit from bullet points or section breaks for the marketplace flow, key constraints, permission notes, and prompt library. Good content, but structure could be improved for an AI agent to parse quickly.

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 is remarkably complete. It covers the end-to-end workflow, edge cases (auth, payment, permissions), the difference between one-off invoke and permanent install, billing operations, and the prompt library's separate functionality. No important behavior appears 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?

The input schema has 23 parameters with 0% schema description coverage, so the description carries the full explanatory burden. It effectively explains the action enum (search, describe, install, invoke, subscribe, cancel, report_bug, request_mcp, search_prompts, get_prompt, publish_prompt) and related parameters like tool_id, prompt_slug, and prompt_vars. However, several parameters (limit, message, immediate, tier_slug, conversation, cancel_reason) receive no explanation, leaving some gaps for the agent.

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 this is 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It describes the core search → describe → invoke flow and distinguishes each action (search, describe, invoke, install, report_bug, request_mcp). This fully differentiates the marketplace tool from siblings like report_bug and 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 on when to use invoke vs. install ('Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use'), when to use report_bug vs. request_mcp, and how subscribe/cancel relate to billing. This directly informs when to select this tool over alternatives.

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 idempotentHint: true, but no explicit read/write/destructive semantics. The description adds context that the conversation array assists reproduction, which is useful. However, it does not disclose side effects like whether the submitted data leaves the session, which would be valuable for a report tool.

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: the first states the purpose with an active verb, and the second provides the key usage instruction. No unnecessary filler or repetition of schema 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?

For a simple 3-param tool with one required param and no output schema, the description covers the essential use case. It could be enhanced by noting privacy implications of including the full conversation array, but overall it's adequate for an agent to invoke the tool 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?

With 0% schema description coverage, the description partially compensates by explaining the 'conversation' parameter (a string containing an array of messages). 'message' is self-explanatory, but 'context' remains entirely unexplained. The description adds value for one of three parameters.

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 purpose with a specific verb+resource: 'Report a bug, missing feature, or send feedback.' It distinguishes this tool from siblings like authenticate or marketplace by covering the exact three workflows (bug, missing feature, feedback) that an agent would use this for.

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 by instructing to 'Include the conversation array with recent messages for reproduction,' but it doesn't explicitly state when to use this vs. alternatives or provide exclusions. Among siblings, none compete for the same purpose, so the lack of exclusions is a minor 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?

The annotations already declare readOnly, idempotent, and non-destructive behavior, so the description does not need to add much. It does clarify the output (versions), which adds value without contradicting 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 a single, clear sentence with no unnecessary words. It is well-structured and front-loaded with the action.

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

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

Given no output schema is present, the description adequately specifies the return content ('current MCP platform and adapter versions'). It is complete for the intended use case.

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 parameter semantics are trivially satisfied. The description adds no parameter-specific information, but none is needed. The schema coverage is effectively 100% (empty set).

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

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

The description uses a specific verb 'Show' and identifies the resource as 'current MCP platform and adapter versions', clearly distinguishing it from sibling tools which cover authentication, connectivity, marketplace, bug reporting, and a specific query tool.

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 self-evident; it is implied that this tool should be used whenever version information is needed. However, there is no explicit mention of when not to use it or alternatives, but given the simplicity, the implied usage is sufficient.

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

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

Annotations already indicate the tool is read-only and idempotent. The description adds valuable behavior beyond this: each query costs prepaid credit, no platform credentials are needed, and the data is not confidential. It also clarifies LGPD responsibility for data usage. These are meaningful additions not covered by annotations.

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

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

The description is concise and front-loaded with the primary action. Four sentences each add meaningful context: what it queries, hosting/authentication, pricing/credit, data nature, and legal responsibility. No unnecessary repetition or fluff.

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 tool's purpose, cost model, and legal context, but lacks specifics about input parameters and return data. There is no output schema, so the description should ideally clarify what the response contains. As a standalone description, it is adequate but leaves gaps in how to actually invoke the query and interpret results.

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 for explaining the parameters (cpf, cnpj, tipo_arquivo_visualizacao). However, the description provides no details about these parameters, their formats, or how they are used. The tool name and context hint at CPF/CNPJ as identifiers, but the description itself adds no parameter-specific 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 performs a query ('consulta') against the official SINTEGRA/SUFRAMA source, and specifies the scope as official Brazilian government data. This distinguishes it from the unrelated sibling tools (authenticate, connect, etc.).

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

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

The description provides clear context for when to use the tool: it is a paid query with prepaid credit, works without platform credentials, and accesses only non-confidential official data. It does not explicitly name alternatives or exclusion scenarios, but the context is sufficient for a standalone tool.

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 is a safe, non-destructive read. The description adds the specific behavioral trait that it returns a snapshot of many components (installed MCPs, statuses, accounts, counts), which is useful. It does not over-explain or introduce hidden side effects. Given annotations cover the safety profile, this is adequate.

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 concise and front-loaded with the key verb ('Returns') and subject ('toolkit state'). Every part of the sentence adds value: it specifies the content and the level of detail. No filler 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 tool has no parameters, no output schema, and simple annotations, the description sufficiently covers what the tool does and what it returns. It lists the categories of information provided (installed MCPs, statuses, accounts, catalog counts), which is complete for a user to decide whether to call it. Minor gap: it does not specify the format or how the counts are presented, but for a zero-parameter info tool this is 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 0 parameters, and the schema coverage is 100% (vacuous). Since there are no parameters, the description's role is limited, but it compensates by explaining what the output includes. The baseline for 0 params is 4 per the rubric, and the description meaningfully describes the result scope, so it meets that expectation.

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: returning current toolkit state with specific details. It uses a specific verb ('Returns') and resource ('toolkit state'), and lists the contents (installed MCPs, connection status, accounts, catalog tool counts). This distinguishes it from siblings like 'show_version' which likely returns only version info, and 'authenticate' which handles authentication.

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 this is for checking the toolkit's current state but does not explicitly state when to use it vs alternatives. Given siblings like 'show_version' may overlap, some guidance on when to call this instead would be valuable. However, the description clearly frames its scope (toolkit state), which helps in selecting it for that purpose.

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