Prefeitura PE Petrolina: Multas

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

Annotations include idempotentHint=true and destructiveHint=false, but the description goes beyond by explaining the mechanism: with no args it returns a link, with token it completes login. It also clarifies that config-based tokens are non-expiring vs session-only. However, it does not mention any side effects such as invalidating previous sessions or the token's visibility, though annotations already cover safety.

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 but packs a lot of information in a few sentences. It is front-loaded with the purpose and then provides instructions. The structure is logical, though slightly run-on with multiple clauses. It earns its place by covering both modes of usage.

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 simplicity (one optional parameter, no output schema) and the annotations providing safety hints, the description is fairly complete. It explains the two paths for authentication and that the tool is intended for IDE agents. No return value is specified, but it is not necessary for this action. Some minor gaps: what happens after successful login (e.g., confirmation), but this is not critical.

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 has one optional 'token' parameter with no description coverage (0%). The description explains that 'token' should be the JWT pasted by the user, and that calling with no args gets the link. This adds semantic meaning beyond the schema, but the description could be more explicit about the token format and that it is required for session login. Given the low coverage, the description partially compensates.

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: authenticating an IDE agent to MCP.AI by logging in in the browser and obtaining an access token. It identifies two modes: permanent config-based and session-only via token parameter. While not explicitly naming sibling tools, the purpose is distinct from siblings like '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 gives explicit guidance on when to use the tool (for initial authentication) and how to use it: either by adding the token to config for permanent access or by passing it as a parameter for session-only login. It also explains the no-args call to get the login link. This is clear and actionable.

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 adds behavioral detail about the two possible return states (authenticated:true with empty pending[], or connect_url for toolkit and per-install URLs). While it does not explicitly state the tool is safe/read-only, the annotations (readOnlyHint: true) already cover that, so the description's extra detail is valuable but not redundant.

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 well-structured: two sentences clearly explaining the tool's purpose and the two possible outcomes. No unnecessary words or jargon, making it easy to parse.

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 params, read-only), the description is complete. It covers both possible states and what the user can expect, without omitting critical information. There are no complex side effects or additional requirements to mention.

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 (0 params, 100% schema coverage). There is nothing to explain, so the description appropriately omits parameter details. This is a perfect score because no parameter semantics 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: it returns connection status and URLs. It uses a specific verb ('Returns') and resource ('connection status and URLs'), which distinguishes it from sibling tools like 'authenticate' 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 when to use the tool (to check connection status), but does not explicitly compare it to alternatives like 'authenticate'. It does provide useful context on expected outcomes under different conditions (all providers connected vs. missing credentials), which aids in deciding 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?

The description goes far beyond the sparse annotations (readOnlyHint=false, destructiveHint=false) by detailing behavioral nuances: invoke works even without installation, returns connect/checkout links for credentials/payment, and clarifies the difference between one-off and permanent installs. It also discloses that writes require owner/admin. This is excellent transparency and 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 long but tightly packed with information. It is front-loaded with the purpose, then logically flows through the core flow, key behaviors, permissions, and the prompt library. Every sentence contributes useful detail, and the structure makes it easy to scan. For a tool with 14 actions, this length is justified.

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 complexity (23 parameters, 14 actions, no output schema), the description is remarkably complete. It explains all actions, their outcomes (e.g., describe returns full profile, invoke returns links), the distinction between installed_in_toolkit vs workspace, and the prompt library. It also covers permission requirements. It leaves little undefined for an agent to use 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?

Schema coverage is 0%, so the description must compensate. It explains the action enum and mentions key parameters like mcp_id, tool_id, and arguments implicitly, but does not define all 23 parameters (e.g., prompt_title, conversation). However, the high-level flow and action explanations give sufficient meaning for the main usage, so it adds significant value over the bare schema.

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 it is the official mcp.ai marketplace — the in-platform catalog of MCPs/tools and the way to run them. It explains the core flow (search→describe→invoke) and distinguishes itself from sibling tools like report_bug or show_version. It also covers the prompt library, so the purpose is specific and non-tautological.

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 when to use each action: prefer invoke for single/occasional use, use install only for permanent additions, list_tools shows what is callable now, and report_bug for feedback. It also states prerequisites (owner/admin for writes) and contrasts with alternatives like request_mcp. This is clear and actionable 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavioral context: the query is hosted by the platform, uses prepaid credits, does not require platform credentials, accesses non-confidential public data, and imposes LGPD data-controller responsibilities on the client. This goes beyond annotation basics, though it does not describe return format or error 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 concise and front-loaded, starting with the core purpose and then adding payment/legal context. Each sentence provides relevant information, though some legal phrasing could be considered boilerplate. It remains appropriately sized for the tool's complexity.

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 two-string input, strong annotations, and unrelated sibling set, the description covers the essential operational details: official source, prepaid payment, no credentials needed, non-confidential data, and LGPD responsibilities. It lacks parameter-level guidance and output shape, but for a straightforward read-only query tool, it is reasonably 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?

Schema description coverage is 0%, and the description does not compensate by explaining the required parameters 'placa' and 'renavam' or their expected formats. While the parameter names are self-descriptive, the description adds no semantic value beyond the schema itself, which is a significant gap for a low-coverage case.

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 specifies the tool's purpose: consulting traffic fines ('Multas') from the official Prefeitura de Petrolina source. It uses a specific verb ('consulta') and resource ('Prefeitura PE Petrolina: Multas'), distinguishing it from unrelated siblings like authenticate, marketplace, and 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?

The description implies usage context: it is for official fines consultation, payable per query with prepaid credits, and requires no platform credentials. However, it does not explicitly state when to use this tool over alternatives or provide any exclusion criteria, leaving usage guidance largely implicit.

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 annotations already indicate readOnlyHint=false, destructiveHint=false, and idempotentHint=true, covering basic safety. The description adds the detail about including the conversation array for reproduction, which is useful context but does not disclose other behavioral traits (e.g., how the report is processed or stored). Since annotations provide guidance, a score of 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 exceptionally concise—exactly two sentences. The first sentence states the purpose, and the second gives a specific usage instruction. There is no fluff or redundancy; 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?

Given the tool's simplicity (3 params, no output schema, no enums), the description is moderately complete. It tells the user what action to take and hints at one parameter (conversation). However, it lacks details on the meaning of 'message' and 'context', and does not explain what happens after a report is submitted. This is a gap for a tool that agents will invoke, making it average.

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 has 3 parameters (context, message, conversation) with 0% coverage in the description. The description only mentions 'conversation array' and does not explain 'message' (the required field) or 'context'. It fails to clarify what content each parameter expects, relying on the schema's default values alone. This is insufficient compensation for the low schema 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 states the tool's purpose: 'Report a bug, missing feature, or send feedback.' This is a specific verb (report) with a clear resource (bug/feature/feedback), and it distinguishes itself from the sibling tools (e.g., authenticate, connect, marketplace), which serve entirely different functions.

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 use cases and even instructs the user to 'Include the conversation array with recent messages for reproduction,' which is a usage guideline. However, it does not explicitly state when not to use this tool or mention alternative tools, 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.

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 the specific subject (platform and adapter versions) but does not disclose return format, potential failure modes, or whether any connection is 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?

A single sentence conveys the entire tool behavior with no filler or redundant phrasing. Key terms are front-loaded and every word 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 no-argument version query with rich annotations, the description is nearly self-sufficient and clearly states what versions are shown. However, without an output schema, it could more explicitly describe the return shape (e.g., a structured object with platform and adapter version strings), so it loses one point.

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 input schema fully covers all parameters (vacuously 100% coverage). Per rubric, a tool with no parameters gets a baseline of 4; the description appropriately does not invent needless parameter detail.

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 a clear resource ('current MCP platform and adapter versions'). It is distinct from sibling tools like authenticate or marketplace, so the purpose is 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 offers no guidance on when to invoke this tool versus alternatives, such as toolkit_info which might also provide environment details. There is no mention of prerequisites, use cases, or exclusions, leaving the agent to infer usage solely from the tool name.

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 and idempotentHint: true. The description adds specificity about what state is returned (installed MCPs, connection status, accounts, catalog tool counts). This adds behavioral transparency beyond the annotations, though it doesn't mention any side effects (none expected).

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?

Single, front-loaded sentence lists all key aspects with no 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?

No parameters or output schema. The description covers exactly what the tool returns: installed MCPs, connection status, accounts, and catalog tool counts. Annotations cover safety and idempotency. For an introspection tool, this is 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?

There are no parameters, so schema coverage is n/a. The description clearly explains what the tool returns, which is the main semantic content. Since there are no params, a high score is appropriate as the description fully communicates tool 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 explicitly states the tool returns the current toolkit state, listing installed MCPs, their connection status, the accounts connected to each, and how many catalog tools each exposes. This is specific and unambiguous, clearly 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 is self-explanatory for when to use it (checking toolkit state), but it doesn't explicitly mention prerequisites or when to use it in a workflow. However, given it's a read-only state tool with no parameters, the usage is obvious from the description and sibling context.

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