Prefeitura PB João Pessoa: Multas

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

The description discloses the browser interaction, permanent vs session-only behavior, and token requirements. It aligns with idempotentHint=true and destructiveHint=false, without contradiction, while revealing more context than the annotations alone.

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?

All necessary information is included in a single, efficient paragraph with no filler. However, the flow is somewhat run-on with multiple clauses in one sentence; breaking it into bullets or shorter sentences would improve readability.

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 one optional parameter and no output schema, the description provides adequate context: login options, permanent configuration advice, and token-based session flow. A slightly more explicit statement of the exact return value would push it to 5.

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 fully explains the optional token parameter, including the expected format (JWT), pasting use-case, and the alternative of no args to get a link. This entirely compensates 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 states what the tool does: enables login via the browser and token acquisition for MCP.AI in IDE agents. It provides distinction via its explicit browser-login and token flow, which is distinct from the 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?

Excellent guidance with explicit usage paths: permanent config header, session-only token paste, and no-arg link retrieval. It gives actionable when-to-use instruction and appropriate invocation patterns.

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 readOnly, idempotent, and non-destructive, so the description adds value by detailing the output structure (authenticated, pending[], connect_url) and conditions for them. It covers two main scenarios but omits partial-connection states and error handling, which are minor gaps.

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—two sentences—with the primary purpose front-loaded and then two specific cases. Every sentence adds meaningful information without redundancy or fluff. It's appropriately sized for the tool's simplicity.

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?

While the description covers the two most likely states (all connected and missing credentials), it does not address partial connections (where some providers have credentials but others don't). With no output schema, the description should more fully explain the response structure. It also omits details about error responses or the exact format of URLs, making it incomplete for edge cases.

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, and the schema covers 100% of them (trivially). Baseline for 0 params is 4, and the description doesn't need to add parameter details. It provides context about output which indirectly helps understand the tool's behavior, but no parameter-specific semantics 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 it returns connection status and URLs, specifying two distinct outcomes (all providers connected vs. missing credentials). It distinguishes itself from sibling tools like 'authenticate' by indicating it's a status-checking tool, not an action-oriented one.

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?

No explicit guidance on when to use this tool versus alternatives. It does not mention calling it before 'authenticate' or 'marketplace', nor does it provide exclusions or prerequisites. The description merely states what it does without contextual placement among siblings.

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 enriches beyond annotations by disclosing that invoke runs MCPs one-off without installing them, returns connect links when auth is needed, returns checkout/top-up links when the wallet is empty, and requires workspace owner/admin for writes. It also reveals that search/describe flag installed_in_toolkit vs installed_in_workspace. No contradiction with annotations (readOnlyHint:false aligns with the described write operations); the rich context far exceeds what structured data provides.

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 unbroken wall of text with no paragraph breaks or structural separation between the catalog flow, billing actions, and prompt library. While nearly every sentence carries valuable content, the lack of organization forces the agent to parse a dense block, significantly harming scannability. Appropriate content, inappropriate packaging.

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 mega-tool with 14 actions, 23 params, and no output schema, the description is remarkably thorough: it covers the core workflow, edge cases (auth required, empty wallet), permission requirements, and the prompt library sub-feature. It implicitly describes outputs (connect link, checkout link, shareable mcp.ai/p/<slug> link) since no output schema exists. Minor deduction for under-documenting some params as noted in parameter semantics.

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 and 23 parameters, the description bears the full burden. It explains the action enum (search, describe, install, invoke, search_prompts, etc.), mcp_id, tool_id, arguments, and several prompt_* params. However, critical params like limit, immediate, tier_slug, cancel_reason, conversation, prompt_targets, and request_details remain undocumented — a gap given the schema offers no help. It covers the most important params but not enough for full compensation.

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 dual role: the mcp.ai marketplace catalog AND the execution engine for running MCPs. It anchors the purpose with explicit examples ('find an MCP that does X', 'consulta um CPF') and outlines the core action=search → describe → invoke workflow, distinguishing the marketplace tool from sibling tools like report_bug and toolkit_info.

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?

Exceptionally explicit when-to-use guidance: 'prefer invoke for a single/occasional use', 'use install only to make an MCP PERMANENT', and 'list_tools lists what is callable right now'. It also names alternatives for specific needs (subscribe/cancel for billing, request_mcp for building a new MCP) and covers the prompt library as a separate sub-domain with its own workflow.

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 declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered, and the description adds genuinely useful context: no platform credentials required, pay-per-query with prepaid credit (cost exposure), data is from official public sources, and LGPD responsibility lies with the client. This exceeds baseline annotation credit, although it could have mentioned rate limits or error/edge-case 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?

It's a single dense paragraph of comma-spliced Portuguese with compliance/legal filler and no structure. It front-loads the core purpose but buries the LGPD/payment details into a wall of text. It's short enough to be arguably 'succinct,' but it's not well-structured — it could use formatting and trimming of redundant legal phrasing.

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 important operational context for a read-only query: official source, public nature of data, pricing model, and LGPD responsibility. For a 2-parameter query tool with no output schema, it's reasonably complete on business/legal context, but it doesn't describe the return format or any functional edge-case behavior. The middle ground of legal info over functionality makes it minimum-viable but not outstanding.

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 makes no attempt to compensate — `placa` and `renavam` are not mentioned at all. While the parameter names are self-explanatory in Portuguese (license plate and vehicle registration), the description adds zero value for parameter semantics, which fails given the heavy burden to compensate for the lack of 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 queries traffic fines ('Multas') from João Pessoa/PB city hall from an official source, with a helpful clarification that it returns the same public data a citizen could access (not confidential data). The verb+resource are clear and it adds meaningful qualifiers (official source, non-secret). It doesn't attempt sibling differentiation, but the steps overall define a clear 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?

Usage is only implied — the description notes the client is the data controller and needs a legitimate (LGPD) purpose, and that each query costs prepaid credit, which hints at when to use it (and to use it sparingly). However, there is no explicit when/when-not guidance, alternatives, or exclusions. The legal caveat gives some orientation, but the guidance is generally 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?

No contradiction with annotations; the description adds the useful note that the conversation array is used for reproduction. However, it does not disclose side effects such as where the report goes, privacy implications of sending conversation data, or what the tool returns. Since annotations already mark the tool as non-destructive and idempotent, some extra context is present but incomplete.

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, front-loaded with the action and followed by the one crucial usage instruction. Every word contributes value, with no redundancy or padding.

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 three-parameter tool with no output schema and minimal annotations, the description covers the core purpose and the key reproduction parameter. It is enough for an agent to select and begin using the tool, but it leaves required message content, optional context semantics, and likely return/confirmation behavior implicit.

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 must compensate for missing parameter documentation. It only clarifies the optional `conversation` parameter as a reproduction aid, while the required `message` and optional `context` are left undescribed. Additionally, it calls conversation an 'array' while the schema declares type string, creating ambiguity about the expected serialized format.

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 opens with 'Report a bug, missing feature, or send feedback', using a specific verb and explicit resource scope. This clearly differentiates it from sibling tools like authenticate, show_version, and pref_pb_joao_pessoa_multas_consultar, none of which handle bug or feedback reporting.

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?

It states the exact scenarios to invoke the tool: bug, missing feature, or feedback. It also instructs the agent to include the conversation array for reproduction. However, it does not explicitly mention when not to use the tool or name alternatives, so it stops 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, idempotentHint, and destructiveHint, fully covering the safety profile. The description adds minimal behavioral context ('current MCP platform and adapter versions') but does not elaborate on output format or other 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?

A single, front-loaded sentence conveys the tool's purpose completely. 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 an extremely simple, zero-parameter, side-effect-free version tool, this description is sufficiently complete. It names the versions returned, uses sound annotations, and does not need extensive 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 schema has zero parameters, so there is nothing to document and the description need not compensate for missing parameter details. The baseline of 4 applies.

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 identifies the exact resource ('current MCP platform and adapter versions'). It is clear and unambiguous, though it does not explicitly distinguish itself from sibling tools like 'toolkit_info'.

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 should be used when current version information is needed, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention 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 readOnlyHint true, idempotentHint true, and destructiveHint false, so the safety profile is covered. The description adds meaningful context beyond the annotations by listing exactly what the toolkit state includes, making the tool's informational scope transparent without 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 entire description is one focused sentence that immediately states the action and then provides an ordered list of returned data. There is no filler, repetition of the tool name, or unnecessary detail.

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 the tool has no parameters, no output schema, and annotations already communicate safety behavior, the description is sufficiently complete. It tells the agent exactly what information can be expected under the state, and no additional detail is necessary for invocation.

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 no parameter explanation is needed. The description fully covers what the tool does without needing to clarify argument semantics, and the schema's empty property set is consistent with this.

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 a specific verb ('Returns') and resource ('current toolkit state'), then enumerates what that state includes: installed MCPs, connection status, connected accounts, and catalog tool counts. This fully distinguishes it from siblings like show_version (version only) and connect/authenticate (mutating actions).

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 as a status/overview tool by saying what it returns, but it does not explicitly say when to use this tool versus alternatives such as connect, authenticate, or show_version. There is no when-not-to-use guidance, but the purpose is clear enough that a capable agent can infer typical usage.

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