MPT MA: Certidão Negativa de Feitos

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

The annotations already indicate idempotence and non-destructiveness, and the description adds meaningful behavior: no args returns a login link, a token creates a session-only login, and a config header creates a permanent non-expiring connection. This goes beyond the structured annotations and clarifies state-changing behavior.

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 reasonably concise, with each sentence contributing a meaningful behavioral or usage detail. It is not overly long, though phrasings like 'log in in the browser' and 'Best: add it to this server's config...' could be tightened for 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?

For a one-optional-parameter tool with no output schema, the description covers the main workflows: browser login, permanent configuration, paste token session login, and no-arg link retrieval. It does not describe the failure case or the standard response, but the context is sufficient for an agent to select and 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 compensates by explaining that token is a JWT pasted by the user and that omitting it triggers the 'get login link' flow. This adds real semantic meaning to the otherwise bare token: string schema field.

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 explains that the tool authenticates by having the user log in through the browser and supply an access token. It distinguishes between permanent configuration-based authentication and session-only token paste, making the purpose and scope unambiguous and distinct from siblings like connect 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 gives explicit usage guidance: use the permanent config header when you want a non-expiring connection, or paste a JWT for a session-only login. It also says to call with no arguments to obtain the login link. It could more directly position this tool against the sibling connect tool, but the primary alternatives are clearly described.

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 false, providing the safety profile. The description adds value by detailing the conditional outputs (authenticated:true and empty pending[] vs connect_url), which is behavioral context beyond the annotations and does not 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?

The description is two sentences, front-loads the core purpose, and contains no filler. Each sentence adds specific output logic, making it highly concise and well-structured.

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 parameters and no output schema, the description explains the return values sufficiently for both states (connected and missing credentials). It conveys the essential fields (authenticated, pending, connect_url) and is complete for a simple read-only status 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, and the description correctly omits parameter details. Per guideline, 0 params 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 connection status and URLs, with specific output for connected versus missing-credentials states. This distinguishes it from sibling 'authenticate' which likely initiates 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 when to use it by describing the output conditions (all providers connected vs credentials missing), but it does not explicitly state that this is for checking status rather than performing authentication. The sibling 'authenticate' suggests the division, but the tool itself does not mention alternatives or 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 only say readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds substantial behavioral context: invoke runs one-off without installing, returns connect/checkout links for auth/payment, writes require workspace owner/admin, and search/describe flag installation state. 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 long but front-loaded with the core purpose and every sentence adds operational value. It could be better structured with bullets or separate sections for the MCP flow vs prompt library, but it avoids fluff and 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?

Given the tool's complexity (14 actions, 23 optional params, no output schema), the description covers the main flows, auth/payment edge cases, permission requirements, and the prompt library. It does not describe return formats for search/list_tools or all parameter combinations, but the overall picture is solid.

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 action enum values, tool_id selection, prompt_slug/vars/body semantics, and invoke's arguments behavior. However, many parameters (limit, query, mcp_id, immediate, tier_slug, cancel_reason, request_details, etc.) receive no description-level explanation, leaving 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 identifies the tool as the mcp.ai marketplace catalog and execution layer, with a specific core flow (search → describe → invoke). It covers many sub-actions but does not explicitly contrast itself with sibling tools like authenticate, connect, or toolkit_info, so it falls short of full sibling differentiation.

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?

Provides explicit when-to-use guidance: prefer invoke for one-off use, use install only to make an MCP permanent, list_tools for currently callable tools, subscribe/cancel for billing, report_bug for feedback, request_mcp when nothing fits. This is model guidance with clear 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 already declare read-only, idempotent, non-destructive behavior. The description adds valuable context: the tool is hosted by the platform, requires prepaid credit, accesses non-confidential public data, and places data controller responsibility on the client under LGPD. This enriches the behavioral profile without contradicting 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 dense sentence combining multiple clauses about hosting, payment, data source, and legal compliance. It is not front-loaded for quick scanning, and while it packs information, it sacrifices brevity. The structure is moderate but could be improved with clearer separation of operational 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?

The description covers payment, legal responsibility, and data source, but leaves gaps: it does not clarify how the parameters (cpf/cnpj) should be provided, what the output contains, or whether one parameter is sufficient. Given the absence of an output schema and low parameter documentation, the description is not fully complete for an agent to use it confidently.

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 the parameters cpf and cnpj. However, it does not explicitly explain what these parameters are for or that they identify the entity to be consulted. The tool name hints at Brazilian IDs, but the description lacks explicit guidance on parameter usage, leaving ambiguity about whether one or both are required.

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 consults a negative certificate (Certidão Negativa de Feitos) from official Brazilian sources. The verb 'consultar' and the specific resource distinguish it from generic sibling tools like authenticate or marketplace, giving it a precise purpose.

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 context (paid per query, no platform credentials, for legitimate LGPD purposes) but does not explicitly state when to use this tool versus alternatives, nor does it mention prerequisites like needing prepaid credit. Sibling tools are unrelated, so no direct comparison is provided, but the context is present.

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 readOnlyHint=false, destructiveHint=false, and idempotentHint=true, so the safety profile is known. The description adds the behavioral expectation that the call should include a conversation array for reproduction, which is useful context. However, it does not disclose what happens after the report is sent (e.g., confirmation, side effects, or whether it just logs), leaving some opacity. It adds moderate value 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 extremely concise, consisting of two short sentences. It leads with the primary purpose (reporting bugs/features/feedback) and then provides the key usage instruction (include conversation). There is no filler, repetition, or unnecessary detail—every word earns its place.

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 tool with three parameters and no output schema, the description is largely sufficient. It states what the tool does, when to use it, and one critical usage detail (the conversation array for reproduction). It does not describe return values, but the absence of an output schema makes that unnecessary. It could mention that calling this tool has no further effect (given idempotentHint) or how reports are handled, but those are not critical for selection and invocation. Overall, it covers the essential contextual needs.

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 no parameter descriptions (0% coverage), so the description must compensate. It explicitly explains the 'conversation' parameter ('Include the conversation array with recent messages for reproduction') and implicitly clarifies 'message' as the content of the bug report or feedback. However, the 'context' parameter is left completely unexplained, and 'message' lacks explicit formatting or examples. The description adds some meaning but does not fully cover all 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's purpose: 'Report a bug, missing feature, or send feedback.' It identifies the action (report/send) and the resources (bug, missing feature, feedback), and it is distinct from its sibling tools (authenticate, connect, etc.), which serve unrelated functions. The verb-noun structure is specific and 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 context for when to use the tool ('Report a bug, missing feature, or send feedback') and gives explicit instruction on how to use it: 'Include the conversation array with recent messages for reproduction.' While it doesn't mention alternatives or exclusions, the sibling tools are dissimilar, so there is no ambiguity. The guidance is sufficient for an agent to decide when to call it.

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds minimal extra context (e.g., 'current' versions imply real-time or latest state). No additional behavioral details (e.g., return format, output structure) are provided, but given the annotations, this is not a critical gap. The description does not contradict any 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, direct sentence with no filler. It conveys the complete purpose without unnecessary detail, making it highly efficient and easy to scan.

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 parameters, no output schema, and rich annotations covering safety, the description is fully sufficient. It states exactly what the tool does, and nothing more is needed to invoke it correctly. The simplicity of the tool justifies this level of detail.

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 the input schema is empty. Per the rubric, 0 parameters establishes a baseline of 4. The description adds no extra parameter meaning because none exist, and none are 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 clearly states the tool's function: 'Show the current MCP platform and adapter versions.' It uses a specific verb ('show') and specifies the exact resource (MCP platform and adapter versions), distinguishing it from sibling tools like authenticate 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?

The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It simply states what the tool does without contextualizing it within the broader workflow. For a trivial read-only tool this may be acceptable, but explicit usage context is missing.

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=false. The description adds valuable context about what data is returned (installed MCPs, status, accounts, tool counts), going 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 a single sentence that is concise, informative, and without redundancy. Every element adds value.

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 parameterless read-only tool, the description fully covers what is returned and what the tool does. No output schema is present, but the description enumerates the key content, making it 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 tool has zero parameters, so the description need not explain parameter behavior. The baseline of 4 is appropriate; no gaps exist.

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 with specific details (installed MCPs, connection status, accounts, and tool counts). The verb 'Returns' is specific and the resource is well-defined, distinguishing it from siblings like authenticate 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?

The description implies the tool is for obtaining an overall status overview of MCPs and connections. It does not explicitly state when not to use it or mention alternatives, but the context is clear enough for an agent to select it when needing toolkit state.

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