INCRA: Disponibilizador de Coordenadas

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

Annotations indicate idempotentHint=true and destructiveHint=false, which align with the description's non-destructive nature. The description adds behavioral context beyond annotations: it explains the two authentication modes (permanent vs session-only), the browser login flow, and the token format (JWT). It doesn't contradict annotations. Slight deduction for not explicitly stating that calling with no args returns a link, but this is implied.

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 with the main purpose. It uses a colon to separate the main purpose from usage details. It's a bit dense with multiple clauses, but every sentence earns its place. Slight deduction for the run-on structure and the parenthetical 'or paste it here' which could be clearer.

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 (1 optional param, no output schema), the description is fairly complete. It covers the two usage modes, the token format, and the permanent vs session distinction. It could mention what the response looks like (e.g., success/failure), but with no output schema, this is a minor gap. The description is adequate for an agent to use this 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 description coverage is 0%, so the description must compensate. It does explain the 'token' parameter: it's a JWT that the user pastes for session-only login. It also explains the no-args case (returns link). This adds meaning beyond the bare schema, though it could be more explicit about the token's format and 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 clearly states the tool's purpose: to authenticate with MCP.AI for IDE agents by logging in via browser and obtaining an access token. It distinguishes itself from siblings by being the only authentication tool among the listed siblings (connect, marketplace, etc.).

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: it explains two methods (permanent config-based token vs session-only token), when to use each (permanent vs session-only), and how to call the tool (with token argument or no args to get link). It also mentions the best practice of adding to config for permanent connection.

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 meaningful behavioral context beyond the read-only/idempotent annotations by disclosing exactly what responses look like in each state: 'authenticated:true and empty pending[]' when connected, and 'connect_url' plus per-install URLs when credentials are missing. This conditional behavior is not inferable from annotations alone. 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 two sentences, front-loads the main purpose, and uses every sentence to convey a meaningful behavioral fact. No filler or repeated schema/annotation content.

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, zero-parameter status tool with no output schema, the description is complete: it specifies both possible result states and the fields returned. It covers enough for an agent to correctly interpret the outcome without external documentation.

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 does not need to explain parameter semantics; per the rubric, a 0-param tool earns a baseline of 4. The description still clarifies that the tool takes no input and simply reports current state.

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 specific verb and resource: 'Returns connection status and URLs,' clearly stating the tool's scope. It differentiates from siblings like 'authenticate' by describing itself as a status/URL retrieval operation rather than a credential or auth action. Conditional outcomes ('authenticated:true' vs 'connect_url') further define its 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?

It gives clear context for when using the tool is appropriate: when you need connection status or missing-credential URLs, including conditional behavior for connected vs. missing credentials. It does not explicitly name alternatives or when-not-to-use it, but the context is sufficient because sibling 'authenticate' clearly covers the auth flow.

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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which align with the description's consultation nature. The description adds context about payment (pay per query, prepaid credit) and data governance (client is controller, no sensitive data), which is useful beyond the annotations. However, it does not disclose technical behaviors like authentication requirements (PKCS12) beyond what schema suggests, or rate limits/availability.

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 that covers multiple topics (purpose, hosting, payment, LGPD) but is somewhat disorganized and dense. It front-loads the main purpose but then dives into legal talk that could be separated. It is not overly long but could be restructured for clarity.

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 relatively complex: 5 params, 3 required, no output schema. The description provides context on the data source and legal aspects but lacks key technical context: what does the tool return? If an output schema is absent, the description should explain the nature of the response. It also does not explain how the authentication works (PKCS12 vs CPF/senha) or the role of 'numero_certificacao'. Given 0% schema coverage, the description is insufficient for an agent to understand the full operation.

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 carries the full burden for parameter semantics. The description mentions 'login_cpf' and 'login_senha' implicitly through 'sem credenciais da plataforma' and the auth context, but it does not explain 'numero_certificacao' or the optional PKCS12 params. The description fails to clarify which parameters are needed for what purpose, leaving the agent to guess.

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 identifies the tool as 'Disponibilizador de Coordenadas' (coordinator provider) from INCRA, a consultation on official sources. It indicates the purpose of querying official Brazilian data but is vague about what 'coordenadas' specifically means (geographic coordinates? certificate numbers?) and does not distinguish it from sibling tools like authenticate or connect, which are likely different 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?

The description explains the business model (prepaid credits, no platform credentials) and that the client controls data for LGPD purposes, implying a usage context. However, it does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites like having an INCRA account or certificates, except indirectly through required params.

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 side effects (one-off execution without bloating toolkit), credential/payment handling (connect/checkout links), permission requirements (owner/admin for writes), and the distinction between installed_in_toolkit vs installed_in_workspace. 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 dense and long (over 200 words) but every sentence adds value, given the 23-parameter and 14-action complexity. It is front-loaded with the main purpose, but a more structured format (bullet points) would improve readability. Still, it is 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?

Comprehensive coverage: core flow, install vs invoke trade-offs, permission model, prompt library, and how search/describe flag installation status. With no output schema, it does not attempt to describe return values, which is appropriate. The description is complete for an agent to use the tool effectively.

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 schema description coverage at 0%, the description compensates by explaining the key parameter `action` and its many values, and explains the flow that gives context to parameters like mcp_id, tool_id, and arguments. Some parameters (immediate, tier_slug, prompt_vars) are not explicitly described, but their purpose is inferable from the action context. Slight deduction for not covering all 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's purpose: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It distinguishes itself from sibling tools (authenticate, connect, etc.) by explaining its broad catalog and execution role.

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 guidance on when to use which action: the core flow (search → describe → invoke), when to prefer invoke over install, and when alternative tools (report_bug, request_mcp) are appropriate. Also explains permission requirements for writes.

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 and destructiveHint=false, so the description doesn't need to cover those. It adds context about including conversation for reproduction, but doesn't disclose what happens after submission (e.g., where the report goes, response 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?

Two sentences, front-loaded with the purpose, and no wasted words. Efficient and clear.

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), the description is adequate but could be more complete. It doesn't explain the 'context' parameter or what happens after submission, but for a feedback tool, it's minimally sufficient.

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. It mentions 'conversation array' and 'message' implicitly, but doesn't explain the 'context' parameter or provide details on format. The description adds some value but leaves gaps.

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: reporting bugs, missing features, or feedback. It distinguishes itself from siblings by focusing on user feedback and reproduction, though 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 implies when to use it (when reporting issues or feedback) but doesn't provide explicit guidance on when not to use it or alternatives. It mentions including the conversation array for reproduction, which is a usage hint.

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 and idempotent behavior, so the description does not need to add much. It accurately reflects a non-destructive operation, and the description does not contradict the annotations. Given the annotations, the bar is lower, and the description is adequate.

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 the essential information without any unnecessary words or 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?

For a simple version-checking tool with no parameters and no output schema, the description fully covers what the tool does. It is complete within its context and does not require additional explanation of return values or side effects.

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 the description does not need to provide any additional parameter explanations. The input schema is empty, and the description is consistent with that, making parameter semantics irrelevant and handled perfectly.

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 MCP platform and adapter versions. The verb 'Show' is specific and the resource is well-defined, 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 implies usage for retrieving version information, but it does not explicitly mention when to use this tool versus alternatives or provide context about expected scenarios. However, the purpose is straightforward and self-explanatory for a version-checking tool.

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 context about the content of the response (installed MCPs, connection status, etc.) but does not disclose any additional behavioral traits like rate limits, authentication requirements, or data volume. It is consistent with annotations, and the added content is useful, but it does not go beyond what annotations imply.

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 starts with the core action ('Returns the current toolkit state') and immediately enumerates the specific data items returned. Every word earns its place, with no filler 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?

With no output schema, the description must explain return values, and it does so by listing the four specific data categories. The scope is simple (no parameters, read-only), so the description covers the essential information. It could be slightly more explicit about the format (e.g., whether it's a list or object), but it is complete for the tool's simplicity.

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 schema coverage is 100% by default. The description does not need to explain parameters, and the baseline for no parameters is 4. The description adds no parameter-specific information, which 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?

The description uses the specific verb 'Returns' with a clear resource ('current toolkit state') and lists the exact components (installed MCPs, connection status, accounts, catalog tool counts). This clearly distinguishes it from sibling tools like authenticate, connect, or show_version, making the purpose unmistakable.

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 toolkit state, but does not explicitly state when to use it over alternatives, nor does it mention any exclusions or alternative tools. It lacks explicit when/when-not guidance but provides enough context to infer the intended use case.

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