MPT PI: Certidão Negativa de Feitos

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

The description discloses behavioral traits like token handling and session vs. permanent login, and notes that calling with no args returns a link. Annotations state idempotentHint=true and destructiveHint=false, so no contradiction. It adds context about token configuration that annotations don't cover, but doesn't mention potential side effects like token expiration specifics beyond 'non-expiring' vs 'session-only'.

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 moderately concise but includes extraneous context like 'MCP.AI for IDE agents (Cursor, etc.)' which is not essential. The structure is slightly informal and could be front-loaded with a clear summary. Every part is relevant, but the sentence could be tightened.

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 with one optional parameter. The description covers the main functionality, usage scenarios, and provides enough guidance for an agent to decide when to use it. It lacks explicit mention of return values, but no output schema exists, so the description is sufficient. The completeness is high given the complexity.

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 coverage for the token parameter is 0%, but the description explains the token parameter's usage and its optionality. It describes how to use the parameter and its role in authentication. However, it doesn't provide format validation or specific details beyond the token being a JWT. Baseline 3 applies since schema coverage is low but description compensates sufficiently.

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 the user by either providing a browser login link or accepting a pasted token. It specifies the action (log in, copy token, add as header) and differentiates from siblings like 'connect' and 'marketplace' by focusing on authentication flow. However, it could be more concise and direct about the core function.

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 explicitly explains two usage scenarios: permanent connection via config header and session-only login via token parameter. It provides step-by-step guidance and mentions alternatives. This is clear and actionable, with no ambiguity.

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 and idempotent behavior. The description adds clarity about the return format and the two possible outcomes (authenticated true with empty pending, or credentials missing with connect URLs). This is consistent with annotations and provides useful behavioral context beyond the basic 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 concise, consisting of two short sentences that convey the essential behavior without unnecessary fluff. It is well-structured, presenting the general purpose first, then the conditional 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?

Given that there is no output schema, the description adequately explains the return value: connection status and URLs, with specific conditions for when each state occurs. It provides enough context for a user to understand what to expect from the tool without additional details.

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 the schema reflects that with an empty properties object. The description does not need to explain parameters since none exist. The schema is complete, so the description adds no missing information.

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: it returns connection status and URLs. It distinguishes itself from sibling tools like 'authenticate' by focusing on status rather than authentication. The conditions for different return values are explicitly outlined.

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 explains what the tool returns under different conditions but does not explicitly state when to use it (e.g., 'Use this to check connection status') or how it relates to alternatives like 'authenticate'. Usage 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?

Discloses key behavioral traits beyond annotations: invoke runs one-off without installing or bloating the toolkit, returns connect/checkout links when needed, install makes MCPs permanent, and prompt links open without login. Annotations are minimal and not contradicted; the description adds substantial operational context.

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 delivered as a long, run-on paragraph with heavy parentheticals and semicolons. Restructuring into bullets or action groups would make the 14-action surface much easier to scan and apply.

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 core marketplace flow, permissions, billing/auth edge cases, and prompt library well. However, it omits return-value shapes for most actions and fails to document the majority of the 23-parameter surface, which is a significant gap for a multi-action router with no 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?

With 23 parameters and 0% schema description coverage, the description carries the full burden, but it only implicitly explains action, mcp_id, tool_id, arguments, and prompt_vars. Most parameters (limit, immediate, tier_slug, cancel_reason, prompt_body, request_details, etc.) are left undocumented, so the agent cannot reliably construct non-core calls.

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 defines this as the mcp.ai marketplace catalog and execution layer, with an explicit core flow (search → describe → invoke) and a separate prompt-library capability. It distinguishes itself from sibling tools by covering catalog discovery, one-off invocation, installation, and billing rather than auth/connect/version 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?

Provides strong when-to-use guidance: prefer invoke for single/occasional use, use install only for permanent toolkit inclusion, and use list_tools to see what is currently callable. It also states that writes require workspace owner/admin and explains the connect/checkout retry flow, giving the agent clear decision rules.

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 the tool is read-only and idempotent. The description goes beyond this by adding that no platform credentials are needed, it's pay-per-query with prepaid credit, and it involves LGPD compliance responsibilities. This adds meaningful behavioral context about cost, authentication, and legal obligations, which are not present in the annotations. Minor omission: no mention of rate limits or response format, but the added value is significant.

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 paragraph with clear front-loading of the main purpose ('MPT PI: Certidão Negativa de Feitos, consulta em fonte oficial') followed by additional context on hosting, payment, and legal aspects. It is concise and well-structured for the information it conveys. The only minor deduction is the slightly run-on nature of the LGPD sentence, but overall it's focused and efficient.

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 query tool, the description adequately covers the purpose, data source, payment, and legal responsibility, which are the key aspects an agent needs to decide when to use it. However, it omits any mention of the required parameters and whether they are mutually exclusive, which is essential for correct invocation. Since there is no output schema, the tool bears full responsibility for parameter clarity, and that is missing, pulling the completeness down.

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, but it does not mention 'cpf' or 'cnpj' at all. While a Brazilian audience might infer these are tax identifiers, the description provides no guidance on when to use one versus the other or their format. This is a clear gap for a tool with no parameter documentation.

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 'Certidão Negativa de Feitos' (Negative Certificate of Records) from MPT PI, using the verb 'consulta' (query) plus the specific resource. This makes it distinct from the sibling tools (authenticate, connect, etc.) which serve entirely different functions. The purpose is unambiguous and directly tied to the tool's name.

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 mentioning it consults official Brazilian sources and is for legitimate purposes under LGPD, but it does not provide explicit when-to-use vs. alternatives or any exclusions. It gives contextual clues (e.g., payment per query) but lacks explicit guidance on when this tool should be chosen over others. This is 'implied usage' rather than explicit direction.

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 indicate idempotentHint=true, readOnlyHint=false, destructiveHint=false. The description does not contradict these. It adds a behavioral detail: including the conversation array helps reproduction. However, it does not disclose any side effects, required authentication, or what happens after submission, which could be relevant. Since annotations cover the safety profile partially, this is adequate but not rich.

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 to the point. It front-loads the purpose and provides a key usage detail. 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?

With no output schema and minimal parameter explanation, the description is incomplete. It does not explain what the response will look like, what constitutes a successful report, or how the 'conversation' array should be formatted (e.g., JSON structure). The description focuses on the input but lacks details on expected behavior or outcomes, which is insufficient for a tool with 3 parameters and no schema coverage.

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 parameter understanding. The description mentions including the conversation array but does not explain the 'message' parameter (which is required) or the optional 'context' parameter. The schema only provides names and defaults ('[]' and ''), which are insufficient. The description adds some value by mentioning the conversation array but does not clarify what 'message' should contain or how 'context' is used.

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 that the tool reports a bug, missing feature, or feedback, and specifies that it includes the conversation array for reproduction. It is specific enough to distinguish from sibling tools that are about authentication, connection, marketplace, etc., although it doesn't explicitly name 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 indicates when to use the tool (to report a bug, missing feature, or feedback) and implies that the conversation array should be included for reproduction. However, it does not provide explicit guidance on when not to use it or mention alternatives, though sibling tools like 'marketplace' could be for feature requests. The context is clear but lacks 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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds minimal behavioral context beyond that, but for a simple version query, this is sufficient. 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 a single, concise sentence that fully conveys the tool's purpose. There is no wasted text, and it is appropriately front-loaded.

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, clear annotations), the description is complete enough. It tells the agent exactly what the tool does, and the annotations cover safety and idempotency. 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 the schema coverage is 100% (empty properties). The description doesn't need to explain parameters, and the baseline for zero-parameter tools is 4. The description adds no parameter info, but none 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 clearly states the tool's purpose: showing the current MCP platform and adapter versions. It uses a specific verb ('show') and resource ('versions'), and it is distinct from sibling tools like 'toolkit_info' 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 when to use this tool (when you need version information), but it does not explicitly state when not to use it or mention alternatives. Given the simple nature of the tool, this is adequate but not 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, covering the safety profile. The description adds value by disclosing the specific categories of information returned (installed MCPs, connection status, accounts, catalog tool counts), which is useful behavioral context beyond the annotations. No contradiction exists.

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, well-structured sentence that leads with the primary verb and object ('Returns the current toolkit state') and then lists the specific data points. Every word contributes to meaning, 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?

Given the tool's simplicity (no parameters, read-only, idempotent) and lack of output schema, the description serves as the primary guide for expected return values. It enumerates all major categories of information an agent would need to decide if the tool is appropriate, making it complete for this context.

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 there are no parameter semantics for the description to clarify. Per the rubric, a 0-parameter tool gets a baseline of 4, and the description appropriately focuses on the return content instead of inventing parameter 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 with the verb 'Returns' and specifies the exact resource: the current toolkit state. It enumerates the included data (installed MCPs, connection status, connected accounts, catalog tool counts), which distinguishes it from sibling info tools 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 implies the tool is used when an agent needs a snapshot of the toolkit's current state, but it does not explicitly state when to use it versus alternatives such as show_version, marketplace, or authenticate. There are no exclusions or alternative tool references, leaving usage context only implicit.

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