CEIS (Inidôneas e Suspensas)

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

Annotations provide idempotentHint=true and destructiveHint=false. The description adds meaningful behavioral context by explaining the two operation modes (permanent header config vs session-only token) and that calling with no args yields a link. This goes beyond what annotations alone convey, with no contradictions.

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

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

The description is three sentences, front-loaded with context about the tool's audience and purpose. Each sentence delivers distinct value: purpose, recommended permanent method, and alternative session method. It is dense but not verbose, with 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 thoroughly covers the tool's use cases and token parameter. However, it does not specify what the tool returns for either call mode (e.g., link, success message, error details). Since there is no output schema, this is a notable but minor gap for an otherwise simple 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 schema only defines 'token' as a string with no description. The description clarifies that 'token' is a JWT to be pasted after user login, and that it is optional since calling with no args retrieves a link. This compensates well for the 0% schema description coverage.

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's purpose: authenticating with MCP.AI for IDE agents. It specifies the verb (log in/authenticate), the resource (MCP.AI), and provides scope by detailing two authentication methods. Although it doesn't explicitly contrast with sibling 'connect', the unique focus on token-based authentication distinguishes it effectively.

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 explicit usage instructions: call with no args to get a link, call with { token: "<jwt>" } for session login, and recommends adding the token to server config for a permanent connection. It does not explicitly state when not to use this tool versus siblings, but the contextual guidance is strong.

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 specify read-only and idempotent hints; the description adds significant context: data is public, no credentials required, payment model, and LGPD compliance responsibilities. No contradiction with annotations.

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

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

The description is three focused sentences, front-loaded with purpose, and every sentence adds meaningful information without repetition.

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 is simple, but the description omits return format and leaves the required-field ambiguity unresolved. Given the low parameter documentation, it should provide more detail on usage and parameters.

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 carries the burden. It mentions CPF/CNPJ but does not explain the 'completo' parameter and creates ambiguity by implying either/or when the schema requires both fields. This is a notable gap.

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 with a specific verb ('Verifica') and resource ('CEIS'), and it is distinct from the generic 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 clear context (no credentials, pay-per-query, public data) but does not explicitly mention when-not-to-use or alternatives. Since siblings are unrelated, the context 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 readOnly and idempotent hints, but the description adds valuable behavioral details: it specifies return values ('authenticated:true', empty pending[], connect URLs) and conditional behavior based on credential presence, going beyond the annotation 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 two sentences, front-loaded with the primary purpose. Every sentence adds meaningful detail—conditional output states and URL info—without any redundancy 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?

For a simple status tool with no params and no output schema, the description adequately covers the main return scenarios (all connected and missing credentials). However, it omits intermediate states where some providers are connected, which could be relevant for full 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?

The tool has no parameters, and schema coverage is 100% (trivially). With 0 params, baseline is 4; the description adds no param-specific info but does not need to, making the score appropriate.

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

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

The description clearly states the tool returns connection status and URLs, using the specific verb 'returns' with a resource ('connection status'). It distinguishes itself from siblings like 'authenticate' by focusing on status rather than performing 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 usage for checking connection status and explains behavior under certain conditions (all connected vs. missing credentials), but it does not explicitly state when to prefer this over alternatives like 'authenticate', nor does it offer when-not-to-use guidance.

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

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

Discloses that invoke runs uninstalled MCPs one-off, returns connect/checkout links when credentials or payment are missing, and requires workspace owner/admin for writes. These behaviors go well beyond the annotations, which only give false hints for readOnly and destructive.

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?

It is a single dense paragraph that front-loads the main purpose and then covers many actions. Every sentence adds value, but the structure could be improved with lists or clearer separation between the MCP marketplace and prompt library sections.

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 large number of actions and 23 parameters, the description covers key behaviors comprehensively: billing, auth, admin permissions, one-off vs permanent execution, and prompt library operations. However, it omits specifics on parameters like resume, cancel_reason, prompt_vars, and does not describe return values, leaving some gaps.

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 explains the action parameter and key values (search, describe, invoke, install, etc.) and how tool_id/arguments are used, but does not detail the other 21 parameters like limit, query, message, or the many prompt_* fields. With 0% schema description coverage, it only partially compensates for the undocumented 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 tool is the official mcp.ai marketplace catalog and the way to run MCPs/tools, with a search → describe → invoke core flow. It also encompasses the prompt library, distinguishing it from sibling tools like authenticate, connect, or report_bug.

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 advises using invoke for one-off usage and install for permanent additions, and explains when to use list_tools, request_mcp, report_bug, and subscribe/cancel. It also notes auth/payment prerequisites and owner/admin requirements.

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=false, idempotentHint=true, and destructiveHint=false. The description adds context about including a conversation array for reproduction but doesn't disclose any additional behavioral traits beyond the annotations. There is no contradiction, so 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 one sentence, front-loaded with the primary purpose, and adds a necessary usage instruction. No wasted words or repetition.

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 no output schema and minimal schema descriptions, the description only explains the action and one parameter. It does not describe what happens after reporting (e.g., response, confirmation, side effects) or any prerequisites. Given the low schema coverage and absence of an output schema, this is incomplete.

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%, so the description must compensate. It explains the 'conversation' parameter as 'recent messages for reproduction,' but does not clarify the meaning or format of 'message' or 'context.' The schema defines conversation as a string, not an array, which could confuse agents. The description only partially addresses 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: 'Report a bug, missing feature, or send feedback.' This is a specific verb+resource, and it distinguishes from sibling tools (authenticate, connect, marketplace) which have unrelated 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 description implies when to use the tool (when reporting bugs/feedback) and provides specific guidance to include the conversation array for reproduction. Since no sibling tool offers similar functionality, explicit alternatives aren't needed. However, it lacks explicit when-not-to-use conditions.

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 description need not repeat those. It adds value by specifying that it displays platform and adapter versions, which is a useful behavioral detail beyond the structured metadata.

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 directly states the tool's purpose without any 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?

The tool is simple and well-covered by annotations, with no parameters and no output schema. The description sufficiently conveys the purpose, though it could optionally mention the output format (e.g., a string or structured object) to be fully complete in the absence of an output schema.

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

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

The tool has zero parameters, so the schema provides no semantics to add. The baseline for 0 parameters is 4, and the description does not need to explain any parameter behavior.

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 resource ('current MCP platform and adapter versions'), clearly distinguishing it from sibling tools like authenticate or connect. There is no ambiguity about what the tool does.

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 intended use is implied for anyone needing version information, but no explicit when-to-use or alternative guidance is provided. For a tool with no parameters and a straightforward purpose, this is acceptable but not exemplary.

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, fully covering the safety profile. The description adds no additional behavioral traits beyond the return content; it doesn't mention latency, permissions, or side effects. Thus it doesn't go beyond annotations, but also doesn't contradict 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?

One informative sentence, front-loaded with the main purpose, with no filler. It efficiently enumerates the return fields without over-specifying.

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 info tool with no parameters and no output schema, the description is complete. It tells the agent exactly what data will be returned and the tool's scope, so there's no critical missing information.

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 100% and there's nothing for the description to explain. Per baseline, it receives a 4 for not needing to compensate.

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 'Returns' and clearly identifies the resource as the 'current toolkit state', enumerating the exact information provided (installed MCPs, connection status, connected accounts, catalog tool counts). This distinguishes it from sibling tools like show_version or connect.

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?

While it doesn't explicitly name alternatives, the description provides clear context that this tool is for inspecting the toolkit's state. The enumerated return fields make its use case obvious, and the sibling tools are action-oriented, so an agent can reasonably infer when to call this. No exclusions are stated, so it falls short of an explicit 5.

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