CONFEA/CREA (Engenharia)

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

The description discloses behavioral traits beyond the annotations, such as the distinction between permanent (via config header) and session-only login, and the fact that calling with no arguments returns a link. The annotations already indicate idempotency, and the description adds useful context about the login flow.

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 fairly compact for the amount of information it conveys, but it is a single dense paragraph. It could benefit from clearer structuring (e.g., bullet points for the two methods), but the information is front-loaded and no sentence is wasted.

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 simple schema (1 optional param) and absence of output schema, the description covers the key aspects: how to authenticate, both permanent and session options, and how to invoke the tool. It doesn't describe the response format, but that's not critical without 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?

Schema coverage is 0%, so the description must compensate. It explains the 'token' parameter by stating it should be a JWT, and clarifies that calling with no arguments gets the link. This gives meaningful semantics beyond the bare schema, though it could be more explicit about the token format and edge cases.

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 to the MCP.AI server. It uses the specific verb 'log in' and describes two modes (link generation and token usage). However, it does not explicitly differentiate from the sibling tool 'connect', so it doesn't fully distinguish alternatives.

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 when to use the permanent header approach vs the session-only token approach. It gives step-by-step instructions for both scenarios. However, it doesn't mention when to prefer this over sibling tools like 'connect'.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds valuable context beyond these: no credentials needed, paid per query (cost implications), and public data with LGPD responsibility for the client. This gives the agent actionable transparency about costs and legal constraints.

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: it opens with the core purpose, then adds short contextual notes on hosting, payment, and legal aspects. Every sentence serves a purpose, with no redundant or fluff content.

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

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

The tool is moderately complex with 3 parameters and no output schema. The description covers the main operation, payment model, and public-data legal context, but the undocumented 'completo' parameter and lack of any return-value information are notable gaps. For a read-only public query tool, it is adequate but not fully complete.

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

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

The description names CPF and RegistroNacional as inputs, which helps map the required parameters. However, the optional 'completo' parameter is completely undocumented, and the schema has 0% description coverage. The description partially compensates but leaves a gap for the optional parameter.

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 queries the professional registration of an engineer or agronomist in CONFEA/CREA using CPF and national registration. This is a specific verb, resource, and input, and it distinguishes the tool from sibling platform utilities.

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: it is hosted by the platform, requires no credentials, and is paid per query with prepaid credit. It also clarifies that the data is public access, guiding appropriate use. While it doesn't explicitly compare to alternatives, no similar sibling tools exist, so 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 declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds valuable behavioral context by detailing what is returned in two distinct scenarios (authenticated:true with empty pending[], or connect_url and per-install URLs), providing concrete response semantics 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?

The description is two concise sentences with the main purpose front-loaded ('Returns connection status and URLs'). Every word 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?

For a zero-parameter, read-only status tool, the description fully covers the two functional states and their corresponding return values. Annotations already cover safety, so no further behavioral disclosure is needed. The description is complete for its simple scope.

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

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

The input schema has zero parameters, so schema coverage is vacuously 100%. Per the rubric, 0 parameters yields a baseline of 4. The description adds no param information because none are needed, which 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 uses a specific verb 'returns' with a clear resource ('connection status and URLs'), and it distinguishes itself from siblings like 'authenticate' by focusing purely on status reporting rather than performing authentication. The conditional behavior further clarifies its role.

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 implies when to use this tool: when you need connection status or authentication URLs. It provides context by explaining the two possible states (all connected vs. missing credentials), but it does not explicitly name alternatives or state when not to use it, which would be stronger 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?

Beyond the annotations, the description discloses key behaviors: invoke works even when the MCP is not installed, it returns a connect link when credentials are needed, a checkout/top-up link when wallet is empty with a retry instruction, and writes require workspace owner/admin. This is rich behavioral context that annotations (readOnlyHint: false, openWorldHint: true) do not provide.

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?

Although long, the description is densely packed and well-structured: it starts with the overall purpose, then the core flow, then key edge cases, then usage recommendations, and finally the prompt library. Every sentence adds distinct information, and the logical progression makes it easy to follow.

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

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

With no output schema and high complexity (14 actions, 23 parameters), the description covers the primary search/describe/invoke flow, credential and payment handling, permission requirements, the distinction between invoke and install, and the prompt library. It gives an agent enough context to select and invoke actions correctly, including the prompt publishing link and billing actions.

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 compensates for the 0% schema coverage by explaining core parameters: action values (search, describe, invoke, install, etc.), mcp_id, tool_id, arguments, prompt_vars, and prompt_slug. However, several parameters like limit, immediate, tier_slug, conversation, cancel_reason, and report_context are not described, leaving gaps for specialized actions.

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

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

The description clearly identifies the tool as 'the official mcp.ai marketplace' and elaborates that it is both 'the in-platform catalog of every MCP/tool' and 'the way to run them.' It distinguishes itself from sibling tools by explaining the core flow (search → describe → invoke) and mentioning the prompt library, making its purpose and scope unmistakable.

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 when-to-use guidance: 'prefer invoke for a single/occasional use' and 'Use install only to make an MCP PERMANENT.' It also names alternatives like request_mcp for new builds, subscribe/cancel for billing, and search_prompts/get_prompt/publish_prompt for prompt text, giving clear directives on tool selection.

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 convey idempotentHint true and destructiveHint false, lowering the burden on the description. The description adds the behavioral expectation that conversation history is needed for reproduction, which is helpful context. Yet it does not disclose side effects, validation, or what happens after submission, so it only partially enriches 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 purpose, followed by a succinct usage tip. Every word earns its place 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?

For a 3-parameter tool with no output schema, the description leaves critical gaps: it does not specify that 'message' is required, misdescribes 'conversation' as an array, and ignores 'context'. While the schema can fill some gaps, the description should compensate for the 0% schema coverage but does not, leaving the agent uncertain about required inputs and types.

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 all parameters. It mentions only the 'conversation' param, but describes it as an 'array' while the schema defines it as a string, which is misleading. The required 'message' param is only implied, and 'context' is entirely omitted. This fails to clarify parameter formats and purpose.

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: 'Report a bug, missing feature, or send feedback.' This is a specific verb-resource pairing and distinguishes it from sibling tools like 'connect' or 'authenticate' which serve different purposes. No ambiguity.

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 a clear usage instruction: 'Include the conversation array with recent messages for reproduction.' It implies the tool is for reporting issues or feedback, and no sibling tool offers this capability, so when to use it is evident. However, it lacks explicit exclusions or alternative recommendations.

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 aligns with these hints and adds the specific subject of the query (platform and adapter versions), but does not disclose any additional behavior such as output format or network calls. For a simple read-only version check, this is sufficient.

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

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

The description is a single, concise sentence that directly states the tool's function without any waste. It is front-loaded and immediately understandable.

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 (no parameters, no output schema, strong annotations), the description fully covers what an agent needs to know. It states exactly what the tool shows and implies the default output is the requested versions. No additional context is necessary.

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 100% vacuously. The description correctly implies no parameters are needed. With no parameters to document, the baseline 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 uses the specific verb 'show' and clearly identifies the resource being shown: 'current MCP platform and adapter versions'. This is distinct from sibling tools like authenticate or report_bug, which serve 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 description provides no explicit when-to-use or when-not-to-use guidance. However, the purpose is so clear that usage is implied: use it when you need version information. No alternative tools are named, but the context of sibling tools suggests no overlap.

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 that this is read-only, idempotent, and non-destructive. The description adds no additional behavioral traits beyond listing return content, which is not behavioral. It doesn't mention performance, authentication, or side effects, but also doesn't contradict 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, front-loaded sentence that efficiently lists the key elements of the return value without any filler. Every phrase adds meaningful information about what the tool returns.

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 it's a parameterless read-only tool with good annotations and a clear sibling set, the description fully covers what an agent needs to know to decide to invoke it. It names the main output categories, which is sufficient for a tool of this simplicity.

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

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

The input schema has zero parameters, so the description naturally adds no parameter-level detail. Per the rubric, 0 parameters earns a baseline of 4. The description focuses on the return value instead, which 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 uses the specific verb 'Returns' with a clear resource: 'current toolkit state'. It enumerates exactly what is included (installed MCPs, connection status, accounts, catalog tool counts), making it easy to distinguish from siblings like 'show_version' or '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 provides clear context on what the tool does, implying it should be used when you need an overview of the toolkit's MCP connections and capabilities. It doesn't explicitly mention exclusions or alternatives, but the sibling tools are sufficiently distinct that the intended usage is evident.

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