Sintegra CCC Completa

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

Annotations already reveal idempotent, non-read-only, and non-destructive behavior. The description adds valuable context: the token is a JWT, no-args invocation returns a link, and the tool supports two modes (permanent and session). This goes beyond the annotations 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 information-dense but well-structured, with a clear progression from context to the recommended approach to the alternative. It could be slightly more concise by trimming the IDE agent examples, but every sentence adds useful detail.

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 one optional parameter, the description covers both invocation modes and the config-based permanent connection. It does not explicitly describe the return values or error cases, but the absence of an output schema makes this acceptable. Overall, it is sufficiently complete for correct 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 only defines an optional 'token' string with no description. The description fully compensates by explaining that the token is a JWT obtained from the browser and by showing exactly how to pass it ('{ token: "<jwt>" }'), as well as the meaning of omitting it (returns a link).

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: to authenticate IDE agents by logging in via browser and obtaining an access token. It uses specific verbs ('log in', 'copy the access token') and distinguishes itself from sibling tools like 'connect' by focusing on authentication for MCP.AI.

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 explicit usage instructions: either add the token to the server config for a permanent connection or pass it as a parameter for session-only login. It also explains that calling with no arguments returns a login link. However, it does not directly contrast with sibling tools, so it slightly misses the 'when vs alternatives' aspect.

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 mark the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral detail by specifying exact return states: authenticated:true with empty pending[] when connected, and connect_url plus per-install URLs when credentials are missing. This explains output logic beyond the annotation basics.

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 long, opens with the core purpose, and each sentence contributes distinct information—the general result and conditional states. There is 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?

For a zero-parameter, read-only status tool without an output schema, the description covers the primary response scenarios and key fields. It explains what happens under full connectivity and missing credentials, giving an agent sufficient understanding of expected behavior.

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 baseline is 4. The description correctly omits parameter details since the input schema is empty and fully covered, and no parameter explanation is needed.

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 'Returns connection status and URLs', a specific verb and resource. It clearly establishes the tool as a status checker, differentiating it from sibling tools like 'authenticate' and 'toolkit_info' by its focus on current connection state and associated URLs.

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 behavioral context by detailing outcomes under two conditions (all providers connected vs. missing credentials). However, it does not explicitly state when to use this tool over alternatives or provide exclusion criteria, so usage guidance remains implicit rather than direct.

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 rich behavioral context (one-off invoke without installation, owner/admin requirements, connect/checkout links requiring user action). However, it explicitly lists `uninstall` among the writes, which is a destructive operation, while the annotation `destructiveHint: false` marks the tool as non-destructive. This is a direct contradiction between description and 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 dense but efficient paragraph. It is front-loaded with the core purpose, uses a clear core-flow diagram (search → describe → invoke), and highlights key behaviors with 'KEY:'. Every sentence earns its place for a tool with 14 actions.

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 full lifecycle: discovery, execution, installation, billing, admin permissions, and the prompt library. It does not explain return values for most actions, but with no output schema and 23 parameters, the description is quite complete. The contradiction between the description and destructiveHint slightly undermines the overall completeness.

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 by explaining key parameter semantics through the workflow: `action` values are enumerated, and `mcp_id`, `tool_id`, `arguments`, `query`, `limit`, and `prompt_vars` are contextualized. However, not all 23 parameters are explicitly mapped; many are left to inference from their names, 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 identifies the marketplace as the official catalog and execution layer for MCPs, with a concrete core flow (search → describe → invoke) and additional responsibilities (install, billing, prompt library). It distinguishes itself from sibling tools by defining its own scope, even though action names like report_bug overlap with sibling tools.

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 explicit guidance on when to use each action: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', and clarifies list_tools for current callable tools. It also explains when to use subscribe/cancel, request_mcp, and the prompt library, giving clear decision 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 establish destructiveHint=false and idempotentHint=true, providing a safety profile. The description adds the context of including the conversation array for reproduction, but it does not disclose whether the report is sent externally, what response can be expected, or if specific permissions are required.

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 long, with the first stating purpose and scope and the second giving an actionable instruction. There is no redundant or filler 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?

For a simple reporting tool with annotations and one required parameter, the description is mostly adequate. However, it leaves the 'context' parameter undocumented and uses the term 'array' for 'conversation' while the schema defines it as a string, creating ambiguity about the expected 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?

Schema description coverage is 0%, so the description must compensate. It explicitly mentions the conversation array (though the schema types it as a string), but message is only implied and context is completely unaddressed. This partial guidance is insufficient for fully correct invocation without additional inference.

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 'Report' and lists three distinct resource types: bug, missing feature, and feedback. This clearly distinguishes it from unrelated sibling tools such as authenticate, connect, and marketplace.

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 clearly states when to use the tool (reporting bugs, missing features, or feedback) and provides an actionable instruction to include the conversation array for reproduction. It does not mention explicit alternatives or exclusions, but the sibling tools are unrelated, so no comparisons are necessary.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is established. The description adds the specific resource (platform and adapter versions) but does not disclose any additional behavioral traits such as potential network calls, latency, or error conditions. This is acceptable for a simple read-only tool, but no extra context is provided.

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 directly states the action and scope. It is concise, front-loaded, and contains no filler or repetition. Every word contributes to 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—zero parameters, no output schema, and strong annotations—the description is nearly complete. It fully explains the tool's purpose. The only minor gap is that it doesn't specify the response format (e.g., structured version numbers), but this is arguably unnecessary for a simple informational 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 schema coverage is trivially 100%. The description doesn't need to add parameter meaning, and the baseline of 4 is appropriate because there is nothing to explain. The description's mention of 'current' versions might imply a dynamic query, but it doesn't require parameter clarification.

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: showing current MCP platform and adapter versions. The verb 'show' is specific, the resource is well-defined, and it distinguishes itself from sibling tools by focusing on version information rather than authentication, connectivity, or marketplace 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?

No guidance is provided on when to use this tool versus alternatives. It simply states what it does, with no mention of exclusions or related tools like `toolkit_info` that might serve a similar informational purpose. Users are left to infer appropriate usage 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?

Beyond the annotations (read-only, idempotent, non-destructive), the description adds that the consultation is paid via prepaid credits, requires no credentials, and operates on public data with LGPD compliance responsibilities. This is useful behavioral context not captured in 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 well-structured, starting with the core purpose, then payment model, then data nature, and finally legal note. It is slightly verbose but each sentence carries relevant information 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?

For a simple query tool with 5 parameters and no output schema, the description covers main inputs, payment, and data sensitivity. However, it lacks explanation of the 'completo' parameter, does not resolve the CPF/CNPJ requiredness contradiction, and does not mention the response format or pagination, leaving some ambiguity for an agent.

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 description mentions CPF or CNPJ, state registration, and UF, but omits the optional 'completo' parameter entirely. Moreover, it ambiguously says 'ou' (or) for CPF/CNPJ while the schema requires both fields, creating a significant contradiction that undermines parameter understanding.

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 complete consultation of the Centralized Taxpayer Registry (CCC) of Sintegra using CPF or CNPJ, state registration, and UF. This is a specific verb and resource, and it distinguishes itself from generic sibling platform tools.

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 querying public-access tax registry data, and mentions the paid, no-credentials model. However, it does not explicitly state when to use this tool over alternatives or when not to use it, only that it accesses public data.

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 covered. The description adds meaningful behavioral context by enumerating the exact content of the returned state (installed MCPs, statuses, accounts, tool counts), which goes beyond the annotation-provided hints.

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, compact sentence that front-loads the primary action and result. Every phrase adds value, with 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?

Despite lacking an output schema, the description fully specifies what the tool returns. Given the simplicity (no parameters, no nested objects) and strong annotation coverage, this is complete and self-sufficient for an agent to invoke and interpret the result.

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 baseline is 4. The description adds no parameter information, but none is needed because the tool requires no 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 uses a specific verb 'Returns' and clearly identifies the resource as 'current toolkit state', listing concrete details (installed MCPs, connection status, accounts, catalog tool counts). This 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 implies usage for inspecting toolkit status, and its contrast with action-oriented siblings is clear. However, it does not explicitly state when to prefer this tool over alternatives or provide any exclusion criteria, so it falls 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.