Conselho Regional de Odontologia MT: Cadastro

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

The description adds meaningful behavioral context beyond the annotations by explaining the browser login flow, the distinction between permanent and session-only authentication, and the no-args behavior. It does not over-explain side effects, and the idempotentHint is consistent with the described 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 informative and organized, front-loading the core workflow and then covering the two invocation modes. It is somewhat dense and could be split into clearer sentences, but every clause contributes useful guidance.

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 one-parameter schema and no output schema, the description covers the essential invocation context, return behavior for no-args, and setup alternatives. It does not specify success/failure responses when a token is passed, but this is a minor gap for an authentication helper.

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?

Although the schema only defines an optional 'token' string with no description, the tool description compensates well by explaining that the token is a JWT pasted from the browser and that omitting the argument triggers a login-link response. This adds practical meaning to the parameter.

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 with MCP.AI by logging in through the browser and providing an access token. It distinguishes the main flow (permanent config vs session-only) from the actual invocation, though it does not explicitly differentiate itself from the sibling tool '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 provides explicit usage guidance, including the recommended permanent configuration via the Authorization header and the alternative session-only flow with a pasted token. It also explains exactly when to call with no arguments (to receive the link) versus with a token, giving clear decision criteria.

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 mark as read-only and idempotent. The description adds value by specifying the exact output in different scenarios (authenticated:true when connected, connect_url when missing credentials), which is not covered by 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?

Two concise, front-loaded sentences that state the primary purpose and clarify key states. No redundancy or irrelevant 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?

Description covers two main scenarios (all connected, missing credentials) but does not address partial connection states or error handling. For a low-complexity status tool, this is sufficient, though not exhaustive.

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, so schema coverage is effectively 100%. The description correctly omits parameter details; baseline of 4 for zero parameters is appropriate.

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?

Description clearly states the tool's purpose: returns connection status and URLs. It also explains behavior for different states (connected vs missing credentials), distinguishing it from sibling tools like authenticate 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?

No explicit guidance on when to use this tool vs alternatives. The description implies its use for checking connection status, but lacks exclusions or references to sibling tools. It provides context on output states but not selection criteria.

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 read-only, idempotent, non-destructive. The description adds that the data is not confidential, the client is the controller for LGPD, and that no platform credentials are required. This goes beyond annotations and clarifies the legal framing and data nature, without 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?

The description is a single well-structured paragraph, front-loading the core purpose. It includes necessary operational details (payment, LGPD) without being verbose. Each sentence adds 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?

The tool is simple (one parameter, no output schema). The description covers the nature of the data (public, not secret), commercial terms (prepaid credit), and legal responsibility. However, it does not elaborate on what the consultation returns or any prerequisites beyond the required parameter. Given the simplicity, it is adequate but not comprehensive.

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 parameter 'inscricao' with 0% description coverage in the schema. The description does not explain what 'inscricao' refers to beyond its obvious meaning (registration number). It adds no semantic detail such as format, length, or examples. Since schema coverage is 0%, the description should compensate, but it does not.

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 a 'consulta' (query) for registration data from the official Brazilian dental council in Mato Grosso. It identifies the resource (cadastro) and the action (consultar). It does not explicitly differentiate from sibling tools, but the siblings are meta-tools (authenticate, connect, etc.), so the purpose is distinct.

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 useful context: it is a paid consultation using prepaid credit, hosted on the platform without credentials, and the data is public/official. However, it does not explicitly state when to use this tool versus alternatives, nor any exclusions. The guidance is more about legal and operational aspects than about selecting between tools.

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 provide readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds substantial behavioral context beyond these: invoke runs one-off without installing or bloating the tool list, credential/payment flows ('invoke returns a connect link... checkout/top-up link'), the install-vs-invoke side-effect difference, and permission requirements ('Writes... require workspace owner/admin'). It also discloses installed_in_toolkit vs workspace flags. No contradiction with annotations is present.

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 and every sentence adds meaningful information, but it is a single unbroken paragraph without bullet points, headers, or line breaks. It front-loads the core value proposition effectively, but the wall of text for a 14-action tool makes scanning harder than it could be. Acceptable size for complexity, but structurally suboptimal.

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 complex tool with no output schema and only two enums, the description covers the core flow, one-off invocation semantics, auth/payment edge cases, permission requirements, the prompt library sub-feature, and the distinction between installed states. It is largely complete for high-level usage. However, it omits parameter-level details, error/retry behavior beyond the wallet top-up case, and return value shape, which are relevant for a tool with 23 parameters.

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% for 23 parameters, and the description only indirectly references tool_id and arguments via the invoke flow. It does not explain parameters such as prompt_vars, prompt_targets, tier_slug, cancel_reason, report_context, conversation, or request_details. While the high-level action semantics are described, the parameter-specific behavior is largely left to the schema, which itself only provides defaults and enums. The description does not compensate for the low 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 identifies the tool as 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It enumerates specific roles: search/describe/invoke for MCP discovery and execution, plus install/list/subscribe/request for management and prompt library operations. This fully distinguishes it from sibling tools like authenticate, connect, or toolkit_info by defining a unique resource (the marketplace catalog) and the core intent-based flow.

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 when-to-use guidance for each major action group: 'Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile... so you pick the right tool_id → invoke RUNS that tool.' It also contrasts invoke vs install ('Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use'), explains list_tools, subscribe/cancel, report_bug, request_mcp, and the prompt library actions. This is textbook usage guidance with clear exclusions and 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 set readOnlyHint:false and destructiveHint:false, implying this is a safe write operation. The description adds the note about including the conversation array for reproduction, which is useful. However, it doesn't disclose what happens after reporting (e.g., whether a ticket is created, response format). Since annotations cover the safety profile, this is acceptable but not exceptional.

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 long, with the primary purpose first and the usage instruction second. Every word earns its place—no fluff or redundancy.

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 feedback/report tool, the description covers purpose and gives one key usage hint. No output schema is expected, and the tool's simplicity means extensive documentation isn't necessary. It's complete enough for an agent to invoke it 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 description coverage is 0%, so the description must fill the gap. It explicitly tells users to include the 'conversation array' for reproduction, clarifying that parameter. However, 'context' and 'message' are not explained. The instruction partially compensates for the lack of schema descriptions, but not fully.

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.' The verb 'report' and resource 'bug/missing feature/feedback' are explicit. It distinguishes itself from sibling tools like 'authenticate' or 'connect' by focusing on bug reporting and 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 description gives clear context on when to use the tool (reporting bugs, missing features, or feedback). It also adds an instruction to include the conversation array, which is practical guidance. However, it does not explicitly mention when NOT to use the tool or name alternatives, though the use cases are well-scoped.

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 that it shows both platform and adapter versions, which is useful content but does not disclose additional behavioral traits such as output format or potential side effects.

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 with no wasted words. It fully communicates the tool's function in one line.

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, trivial function) and the strong annotations, the description is largely sufficient. It lacks an explicit mention of the return format, but for a version-checking tool, the current wording adequately conveys the expected behavior.

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 takes zero parameters, so the empty schema fully documents the input. Per baseline for 0 params, a score of 4 is appropriate; no additional parameter explanation 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 uses the specific verb 'Show' and clearly identifies the resource: 'current MCP platform and adapter versions.' This is unambiguous and distinguishes the tool from sibling utilities like authenticate or 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 purpose is clear enough that an agent would know when to call it (when version info is needed), but there is no explicit guidance on when to use it versus alternatives, nor any mention of common contexts like troubleshooting or compatibility checks.

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 read-only and idempotent behavior. The description adds specific details about the content of the state (MCPs, connections, accounts, catalog counts), which provides useful context beyond the generic annotations. No 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?

A single, focused sentence with no redundant words, effectively front-loading the action and listing key components.

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 no output schema, the description lists the main return categories but omits exact structure or example. However, for a simple read-only tool with no inputs, it provides enough detail for an agent to know what to expect.

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 the description cannot add meaning beyond the schema. The baseline for zero-parameter tools is 4, and the description correctly avoids specifying 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 returns the current toolkit state and enumerates the specific data items (installed MCPs, connection status, accounts, catalog tool counts), making it distinct from sibling tools like authenticate or connect which perform 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 does not explicitly mention when to prefer this tool over alternatives, though it's clearly an introspection tool. The absence of alternative references means the agent must infer usage from the tool's purpose, so it's only implied.

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