Prefeitura RS Montenegro: Certidão Negativa de Débitos

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

The description goes beyond the annotations by stating that a no-argument call returns a link, that passing a token creates a session-only login, and that permanent access is better handled through a configured Authorization header. This is valuable context; only the detailed return shape after the session call is not described, but annotations already cover idempotency and non-destructiveness.

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 dense but stays focused; every sentence contributes either usage context or invocation instructions. It is not as terse as possible, but the extra guidance about permanent versus session-only login is valuable enough to justify the length.

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 parameter shape and absent output schema, the description covers the main expected usage modes and what the no-argument call returns. The only minor gap is not stating the exact response payload or success/failure behavior when passing a token, but this remains usable for practical agent 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 token parameter has zero schema description coverage, so the tool description must compensate. It does so reasonably by explaining that the token is a JWT, should be pasted from the browser, and is used for session-only login. It also clarifies that the parameter is optional since calling with no arguments gets the link.

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 authentication mechanism for the MCP server, specifying an actionable workflow: log in, obtain a token, and either configure it permanently or pass it for a session. It distinguishes itself from siblings by focusing specifically on authentication and login, not general connection or metadata operations.

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 explicitly explains when to use the tool: with no arguments to get a login link, and with a token for session-only login. It also directly contrasts this with the recommended permanent setup via server config, giving the agent a clear decision path between two authentication modes.

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 readOnly, idempotent, non-destructive. The description adds conditional return behavior: when all providers connected returns authenticated:true and empty pending[], when credentials missing returns connect_url and per-install URLs. This goes beyond annotations by describing response conditionsais. No contradiction.

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?

Two sentences, front-loaded, no fluff. Describes purpose and conditional behavior efficiently.

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 read-only status tool with no parameters and no output schema, the description covers the two main cases (all connected vs credentials missing) and provides enough context. Could be slightly clearer about the exact meaning of pending[] or URLs, but it's adequate.

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?

No parameters exist, so baseline is 4. Description doesn't need to explain parameters; it's about the return 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 clearly states the tool's purpose: 'Returns connection status and URLs.' It specifies the exact resource (connection status) and the output (URLs). It distinguishes itself from sibling tools like 'authenticate' by focusing on status rather than performing authentication, making it a distinct read-only check.

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: to check connection status and determine if credentials are missing. It gives conditional behavior (when all providers connected vs when credentials missing) but doesn't explicitly name alternatives or say 'use when...'. The mention of 'connect_url' for missing credentials implies it might be a precursor to authentication, but it doesn't explicitly say 'use to check if you need to authenticate' or name an alternative tool. So it's clear context but lacks explicit exclusion 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?

Goes well beyond annotations by disclosing that invoke works even when the MCP is not installed, returns connect or checkout links when credentials/funds are missing, requires workspace owner/admin for writes, and distinguishes one-off from permanent installs. This is rich behavioral context not available from structured fields.

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 densely informative and front-loaded with the core flow. Each section adds value, though the paragraph style and examples make it heavier than necessary; a bulleted structure could improve scannability.

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 23-parameter, multi-action router with no output schema, the description is unusually complete: it covers discovery, profiling, invocation, installation semantics, auth/billing behavior, permissions, prompt-library operations, and fallback request_mcp. It explains return links for key flows, which compensates for the missing 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 carries the burden for parameter meaning. It explains the action enum values and high-level flows (search, describe, invoke, prompt actions), but does not clarify many individual parameters such as limit, query, immediate, arguments format, tier_slug, or prompt_* fields beyond their obvious names.

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 catalog and execution layer, with a specific core flow (search → describe → invoke). It distinguishes from siblings by focusing on discovery, profiling, and one-off execution, though it is broad because it also covers installs, billing, and prompt-library 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?

Provides explicit when-to-use guidance: prefer invoke for single/occasional use, use install only to make an MCP permanent, and search/describe for discovery. It also gives alternatives within the tool (subscribe/cancel, report_bug, request_mcp) and describes the core decision path from capability request to execution.

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 valuable context: it is hosted by the platform, requires prepaid credits, and clarifies that the data is not confidential and is the same available to citizens. It also addresses LGPD compliance, which is important for data privacy. This goes beyond the annotations and provides useful behavioral 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 a single paragraph with three sentences, each providing distinct information: what it does, how it is hosted and paid, and data privacy context. It is concise and front-loaded with the core purpose. It could be slightly more structured, but it is efficient and not verbose.

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 (2 parameters, no output schema, no nested objects), the description covers the essential aspects: purpose, source, payment model, and legal context. It does not explain the return format, but since there is no output schema, that is a minor gap. The description is complete enough for an agent to understand what the tool does and the key operational details (prepaid credits, LGPD).

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 2 parameters (cpf and cnpj) with 0% description coverage, so the description must compensate. The description does not explain the parameters at all, but the parameter names are self-explanatory (CPF and CNPJ are common Brazilian identifiers). The description mentions 'consulta' and the context of CND, which implies that either CPF or CNPJ is needed, but it does not specify that they are alternatives or that at least one is required. Given the low schema coverage, the description should have provided more detail, but the names are clear enough for a baseline.

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: it queries an official source for a Certidão Negativa de Débitos (negative debt certificate) from the Montenegro city government. It specifies the resource (Prefeitura RS Montenegro) and the action (consultar), which distinguishes it from the sibling tools that are general platform utilities (authenticate, connect, etc.). However, it could be more explicit that it returns the certificate status or data.

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 querying official Brazilian sources, and it mentions payment via prepaid credits, which suggests when to use it (when a user needs a CND for Montenegro). It does not explicitly state when not to use it or mention alternatives, but the sibling tools are unrelated, so the context is clear enough. It lacks explicit guidance on prerequisites like having credits or the need for CPF/CNPJ.

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 read-only, destructive-Hint, and idempotence. The description adds one behavioral detail: the conversation data is needed for reproduction. It does not disclose what happens after submitting, such as whether a ticket is created or if data is sent externally, leaving some side-effect details unexplored.

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 exactly two sentences: the first states the purpose, the second gives necessary reproduction guidance. Every sentence contributes value, and the most important information is 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 three parameters and no output schema, the description is thin. It does not explain what the agent or user will receive after a successful report, how to structure the optional `context` parameter, or how the conversation should be serialized. The purpose is clear, but the full operational picture is incomplete.

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 coverage, the description must carry parameter meaning. However, it refers to a 'conversation array' while the schema types conversation as a string (defaulting to '[]'), which is misleading for an agent. The `message` and `context` parameters are not clarified beyond the tool's broad 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 opens with a clear verb-resource pairing: 'Report a bug, missing feature, or send feedback.' This captures the purpose explicitly and distinguishes the tool from all six siblings, none of which relate to bug reporting or feedback.

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 first sentence directly states when to use the tool (for bugs, missing features, or general feedback), and the second sentence provides actionable guidance about including the conversation for reproduction. It lacks explicit exclusions or alternatives, but the unrelated sibling set makes those less necessary.

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 read-only, idempotent, and non-destructive behavior. The description is consistent with these traits and does not contradict them, but it adds little beyond what annotations provide. Given the annotations, the bar is lower, and the description suffices.

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 conveys all necessary information. It is 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?

Given the tool's minimal complexity (no parameters, no output schema), the description is complete. It clearly states what the tool does without needing additional context or return value explanations.

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 the description has nothing to explain beyond the schema. The schema coverage is 100% (no parameters), meeting the baseline. The description does not need to add parameter semantics.

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: showing the current MCP platform and adapter versions. It is specific and unambiguous, leaving no doubt about what the tool does.

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 explicitly mention when to use this tool versus alternatives, but given its simple nature as a version checker, such guidance may be unnecessary. It is adequate but not explicit about context.

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 description adds value by detailing the specific content returned (installed MCPs, connection status, accounts, catalog tool counts). It does not contradict the annotations and provides useful context beyond the safety 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 a single, front-loaded sentence that begins with the action and resource, then lists the key outputs. It is concise, with no wasted words, and clearly structures the information.

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, parameterless read-only state inspection tool with no output schema, the description is complete. It enumerates exactly what will be returned and is sufficient for an agent to understand the tool's function and expected output.

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 correctly omits parameter explanations. Baseline for 0 params is 4, and the description does not need to add parameter semantics beyond what is already evident.

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 a specific verb 'Returns' and a precise resource 'toolkit state', enumerating the exact details returned (installed MCPs, connection status, accounts, catalog tool counts). It distinguishes itself from sibling tools like show_version (which likely shows version info) and connect/authenticate (which are 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 for inspecting the toolkit's overall state, but does not explicitly mention when to use this tool versus alternatives like show_version or connect. There is no stated when-not-to-use or alternative recommendation, so it relies on inference.

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