Cadastro Ambiental Rural: Demonstrativo

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

The annotations already indicate this is not read-only, not destructive, and idempotent. The description adds context about persistence: config-based header gives a permanent non-expiring connection, while passing a token gives session-only login. This goes beyond the annotations by describing the outcome of each method, satisfying the disclosure burden.

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 structured with a lead-in, then 'Best' and 'Or' options, keeping related details together. It's not overly verbose for the information delivered, though it could be tightened slightly. Each sentence contributes to the usability.

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 tool with one optional param and no output schema, the description explains both the input (token) and behavior (returns link when no args). It mentions the two use cases and the config alternative. No further context is needed 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?

The single optional parameter 'token' is described as a JWT for session login, adding meaning beyond the schema's bare 'string' type. The description clarifies that calling without args yields a link, and calling with token logs in. Since schema coverage is 0% but the description compensates fully, this scores well.

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 that this tool authenticates the MCP server for IDE agents, differentiating it from sibling tools like connect or marketplace. It specifies the action (login) and the resource (server connection), making its purpose 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?

It provides explicit instructions on when to use this tool (for authentication) and explains two invocation modes: passing an optional token for session-only login, or no args to get a link. It also suggests a config-based permanent method. However, it doesn't mention when not to use it or alternative authentication methods beyond these two, so it lacks explicit exclusions.

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, so the description doesn't contradict themanding these are safe read operations. The description adds useful context: it confirms it's not confidential data must mention LGPD control responsibilities, which is beyond the annotations. However, it doesn't detail rate limits, pagination, or error behavior, but given the annotation coverage, a 3 is fair.

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 one long paragraph of three sentences. It includes essential context (official source, prepaid credits, LGPD) but is front-loaded with the tool name in the first sentence, which is redundant. It could be split into bullet points or shorter sentences for readability. Every sentence adds value, but structure is not optimized.

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), so the description doesn't need to explain much. However, it omits critical information: the format of 'car' (although obvious to Brazilian domain), what data is returned, and any legal or usage caveats beyond LGPD. Given that annotations cover safety, the description should still specify the input format and response nature, which are missing.

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 input schema has only one parameter 'car' with no description, and schema_description_coverage is 0%. The description does not explain what 'car' is (presumably the CAR code) or give any format example. The description adds no semantic meaning beyond the placeholder name, leaving the agent guessing about the required data.

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 says it is a 'Cadastro Ambiental Rural: Demonstrativo, consulta em fonte oficial.' It clearly identifies the resource (CAR demonstrativo) and that it queries an official source, but the verb 'consulta' (consult/query) is generic and doesn't specify whether it fetches a report, calculates a value, or retrieves a document. It distinguishes from siblings by being the only one about CAR, but the exact action remains vague.

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 mentions it's hosted on the platform, requires prepaid credits, and queries official Brazilian sources, but it does not specify when to use this tool versus alternatives. There are no sibling tools for data queries, so there is no guidance on selection, and no prerequisites (e.g., need for valid CAR code) or exclusion conditions are stated.

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 tool's safety profile is known. The description adds valuable context by specifying return values under two scenarios (all connected vs. missing credentials), but it does not disclose any additional implications (e.g., what 'pending' signifies or if partial connectivity exists).

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-loaded with the core purpose, and every sentence adds value. It is concise and well-structured 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?

Given that there is no output schema and the tool is simple, the description adequately covers the main return values and conditional cases. It lacks exhaustive detail (e.g., what happens if some but not all providers are connected), but it provides enough for an agent to understand the basic outcomes.

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 zero parameters, the input schema is empty, and the description does not need to explain parameters. The baseline for zero-parameter tools is 4, and the description correctly avoids redundant info.

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 with specific verbs ('returns connection status and URLs') and provides concrete behavioral detail (e.g., returns authenticated:true when all providers are connected). This distinguishes it from siblings like 'authenticate' or '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 usage for checking connection status by describing its behavior under different credential conditions. However, it does not explicitly state when to use this tool versus alternatives like 'authenticate', lacking explicit exclusions or alternative recommendations.

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?

With only sparse annotations (readOnlyHint=false, idempotentHint=false), the description adds meaningful behavioral detail: invoke runs the tool one-off without adding the MCP to the toolkit, writes require workspace owner/admin, invoke may return connect/checkout links, and prompt share links open without login. It does not disclose all side effects of subscribe/cancel/resume or rate limits, but it provides substantial context beyond the 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 dense: nearly every sentence adds a distinct fact about search/describe/invoke, install-vs-invoke, auth requirements, or the prompt library. It is front-loaded with the core purpose and flow, though the single-paragraph structure makes it harder to scan than a bulleted breakdown would be.

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 14-action, 23-parameter tool with no output schema and minimal annotations, the description covers the main lifecycle and edge cases well, including auth, payment, and one-off execution. Still, it omits the return shapes for search, list_tools, install, subscribe, and request_mcp, and does not clarify the semantics of parameters like immediate, resume, or conversation, which are important for correct 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 schema has zero property descriptions, so the description must carry the parameter-semantics burden; it does a solid job for the central parameters by explaining the action enum, the importance of tool_id for invoke, and how prompt_vars/prompt_slug fit into get_prompt/publish_prompt. However, with 23 parameters, several remain only inferable from their names (immediate, conversation, prompt_targets, report_context), so it is not fully compensating for the 0% 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 identifies the tool as 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them,' and explains the core search → describe → invoke flow. It is unambiguous about the overall resource and scope, though it does not explicitly differentiate itself from sibling tools like report_bug or connect, and it also bundles the prompt-library feature into the same tool.

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: 'prefer invoke for a single/occasional use' versus 'Use install only to make an MCP PERMANENT', names request_mcp for cases where nothing fits, and explains the retry pattern when invoke returns a connect or checkout link. This is strong, actionable guidance that distinguishes between the many actions the tool can perform.

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 idempotent and non-destructive behavior, and the description does not contradict this. It adds useful context about including conversation history for reproduction, implying the report is sent to developers. However, it does not detail side effects like ticket creation or notifications.

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) and directly to the point. It avoids unnecessary wording and provides the key instruction about including conversations.

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?

There is no output schema, so return values need not be explained. However, the description does not specify what happens after the report is submitted (e.g., confirmation, ticket ID) or elaborate on the 'message' and 'context' parameters. It is adequate for simple understanding but lacks details that would make it fully 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 coverage is 0% (no descriptions for any parameter). The description only mentions the 'conversation' parameter and its purpose, leaving 'message' and 'context' undefined. Given the low coverage, the description should compensate by explaining all parameters, 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 the tool's function: reporting bugs, missing features, or feedback. It distinguishes itself from other tools in the set (e.g., authenticate, show_version) by focusing on user-reported issues.

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 (for reporting issues) but does not explicitly contrast with alternative tools or mention exclusion criteria. It is clear enough for most cases, but could be more explicit about scenarios where other tools would be more appropriate.

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, describing a safe, non-mutating operation. The description adds the useful context that it reports current versions, which aligns with the annotations. No additional behavioral warnings or side effects are necessary for this simple version lookup.

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 that fully communicates the tool's function with no filler, tautology, or redundant information. It is front-loaded and appropriately concise.

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 has no parameters, no output schema, and strong annotations, the simple description fully covers what the agent needs to know: that invoking show_version retrieves current platform and adapter versions. No additional details are required for such a lightweight tool.

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 0 parameters and the schema coverage is 100%, so there is no parameter ambiguity. Per the rubric, a zero-parameter tool receives a baseline of 4; the description appropriately requires no param-level elaboration.

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 clearly identifies the resource: 'current MCP platform and adapter versions.' Among siblings like authenticate, connect, and toolkit_info, none directly overlap with version reporting, 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?

No explicit when-to-use or alternative guidance is provided. The intended usage is implied: call this tool when you need to check current platform and adapter versions. This is sufficient for a simple no-parameter tool but lacks explicit context or exclusions.

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, idempotentHint, and destructiveHint as false. The description adds no extra behavioral details (e.g., rate limits, side effects), but does not contradict the annotations. Since annotations cover the safety profile, the description scores at the neutral baseline.

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 covers all key aspects without redundancy. It is well-structured and free of unnecessary 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?

The tool is simple with no parameters and no output schema provided. The description sufficiently enumerates what the returned state includes, making it complete for an agent to understand the tool's function.

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 schema coverage is effectively 100%. The description adds no parameter-specific information, but there is nothing to document. Per the rubric, with 0 params the baseline is 4.

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, including installed MCPs, connection status, connected accounts, and tool counts. It is specific and distinct from sibling tools like authenticate or connect, which have different purposes.

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 when one needs an overview of the toolkit's status. It does not explicitly contrast with alternatives, but given the read-only nature and lack of similar info tools (show_version likely targets version only), the context is reasonably clear. Slight deduction for not explicitly stating when to prefer this over others.

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