SNCR: Emissão do Certificado de Cadastro do Imóvel Rural (CCIR)

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

Annotations already indicate idempotency and non-destructiveness, so the bar is lower. The description adds valuable behavioral context: browser login flow, session-only vs permanent token, and that calling with no args returns a link. It does not contradict 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 moderately long but every sentence contributes: context, best practice, and usage scenarios. It's front-loaded with the tool's purpose. Slight verbosity but no wasted words.

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 should hint at return values; it mentions 'get the link' but doesn't detail the response. It covers all major flows (browser, config, session token) and error handling is absent but acceptable for a simple auth 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?

Schema coverage is 0%, so the description carries full responsibility. It clearly explains the 'token' parameter as a JWT to paste for session login, and describes the no-argument call to get a link. This precisely defines parameter semantics beyond the bare schema.

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: authenticating IDE agents to MCP.AI. It specifies the exact actions (browser login, token paste) and distinguishes itself from sibling tools by focusing solely on authentication. The verb 'log in' and resource 'MCP.AI' are explicit.

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 clear guidance on when to use the tool: for session-only login via pasted token, or with no args to get a link. It also recommends the alternative of configuring a header for a permanent connection, which acts as a when-not/alternative. While it doesn't explicitly mention sibling tools, the context is sufficient for an agent to decide.

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, and the description adds meaningful response behavior beyond those: the authenticated:true/pending[] state and the connect_url/per-install URL fallback. It does not contradict the annotations and gives useful conditional 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 two concise sentences, front-loaded with the core purpose and followed by the two key response states. Every sentence adds meaningful information without 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?

Given the low complexity, zero parameters, and existing annotations, the description is nearly complete. It covers both main response states despite the lack of an output schema, though it could briefly clarify what 'pending' or 'per-install URLs' represent.

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 baseline is 4. The description correctly focuses on the returned data rather than parameter details, and no parameter semantics are 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 a specific verb and resource: 'Returns connection status and URLs.' It clearly identifies what the tool does and differentiates it from siblings like authenticate by describing a status/URL introspection function rather than an action.

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 the tool by describing the two response conditions, but it does not explicitly provide usage guidance or mention alternatives. It does not say 'use authenticate when you need to establish credentials' or clarify when not to use this tool, so the usage context is only implied.

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?

Beyond annotations, it discloses one-off invoke semantics, behavior when credentials are missing (connect link), behavior when payment is needed (checkout/top-up link), permanent vs. temporary install effects, installed_in_toolkit vs. installed_in_workspace flags, and prompt share links opening without login. This is rich, non-obvious behavioral context that the sparse annotations do not provide, and there is 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?

The description is long but organized around core flow, key invoke behavior, install vs. invoke, billing/reporting actions, and the prompt library. Some redundancy and run-on sentences (e.g., 'without adding the MCP to the toolkit and without bloating the tool list') make it less crisp than it could be, but the density is largely justified by 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?

The description covers the main workflow, permission requirements, billing/auth edge cases, installed flags, and prompt-library outputs. However, with no output schema, it leaves some return-value expectations implicit (e.g., success response shape for search, list_tools, and invoke success), so it is not fully complete for a tool of this complexity.

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% with 23 optional parameters, so the description carries a heavy burden. It does explain the action enum and key workflow parameters like tool_id, prompt_vars, prompt_slug, and prompt_body, but many parameters (arguments, conversation, immediate, tier_slug, message, request_details, report_context, etc.) are left unexplained. It partially compensates for the schema gap 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 identifies the tool as the mcp.ai marketplace: a catalog and execution layer for MCPs, plus a prompt library, and lays out the search → describe → invoke flow. It names the main sub-actions precisely, but bundles many capabilities under one tool and does not explicitly distinguish the marketplace's report_bug action from the sibling report_bug tool, so it stops short of a perfect 5.

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 guidance: prefer invoke for single/occasional use, use install only to make an MCP permanent, use request_mcp when nothing fits, and use list_tools/search_prompts for their respective purposes. It also notes that writes require workspace owner/admin, making when-to-use and when-not-to-use very clear.

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, readOnlyHint=false, destructiveHint=false. The description does not disclose any additional behavioral traits such as network calls, rate limits, or side effects. It does not explain what happens after reporting (e.g., if a ticket is created, if it's asynchronous). The description adds minimal context beyond the annotations, so it falls short.

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, concise, and front-loaded with the primary purpose. Every sentence adds value: the first defines the action, the second gives a usage hint. It is not verbose, though it could be more informative without sacrificing conciseness.

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 complexity (3 params, 0% schema coverage, no output schema) and the lack of annotations, the description is insufficient. It does not explain the expected format of the conversation array, what happens after reporting, or any error handling. The tool is a reporting mechanism, so it should clarify how to structure the input and what the user can 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?

Schema description coverage is 0%, so the description must compensate for the parameters. It mentions 'conversation array' but does not explain the format of 'context' or the exact structure of 'conversation' (it says 'recent messages' but no JSON format). The 'message' parameter is self-explanatory, but the other two are vague. This is a clear gap.

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.' It uses a specific verb (report/send) and identifies the resource (bug, feature, feedback). It distinguishes from siblings by the action of reporting, though it doesn't explicitly differentiate from other feedback-related tools, but none seem to overlap.

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 no guidance on when to use this tool versus alternatives. It only says what the tool does (report bugs/feedback). There is no mention of when not to use it, nor any comparison to sibling tools like sncr_ccir_consultar or toolkit_info. The instruction to include the conversation array is a usage hint, but not enough to score higher.

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 the tool as read-only, idempotent, and non-destructive. The description adds that it reports 'current' platform and adapter versions, but does not describe the response format or any 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 sentence contains exactly the necessary information: what is shown and for which components. There is no wasted language or redundancy with the title.

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 zero-parameter, read-only version query with strong annotations, the description is nearly complete. It states the exact subject of the output (platform and adapter versions), though it omits any detail about result formatting.

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 baseline is 4. The description appropriately makes no attempt to explain parameters that do not exist.

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 names a clear resource ('current MCP platform and adapter versions'). This is more precise than the title 'Show Version' and distinguishes it from sibling tools 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 gives no explicit guidance about when to use this tool versus any alternative. There are no stated exclusions or context clues about when this version information might be needed.

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, idempotentHint, and destructiveHint. The description goes beyond these by explaining that the tool is hosted on the platform, requires no platform credentials, is paid with prepaid credit, and that the client is the data controller per LGPD. It also clarifies that data is not confidential. This adds valuable behavioral context without contradicting 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 relatively concise for the information it conveys, leading with the main purpose and then adding payment and data responsibility details. It is structured logically and does not contain superfluous text, though it could be slightly tighter.

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 business context (payment, data control, official source) but omits crucial operational details: what the exact input parameters mean, what the output format is (no output schema), and any limitations or expected response structure. For a paid consultative tool with 6 parameters, this 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?

Schema coverage is 0%, and the description does not explain any of the 6 parameters (e.g., codigo_imovel, uf_sede, municipio_sede). It only mentions the overall purpose. This is a severe gap for a tool with multiple required inputs, as the agent cannot infer what values are 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 clearly states the tool's function: 'Emissão do Certificado de Cadastro do Imóvel Rural (CCIR), consulta em fonte oficial' (Issuance/consultation of the CCIR certificate in official source). It uses a specific verb and resource, and is distinct from generic 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?

While no explicit alternative tools are mentioned, the description provides clear context: this is a paid consultation from an official source, without platform credentials. It implies when to use (when needing CCIR data) but does not explicitly state when not to use or list alternatives. Since no sibling does a similar function, this context is sufficient.

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 idempotentHint=true, covering safety. The description adds value by specifying exactly what information is returned (installed MCPs, connection status, accounts, catalog tool counts), which is beyond the annotations. It does not mention response format or potential error conditions, but for a read-only status tool, this 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 succinct sentence that directly states the purpose and the key pieces of information returned. No unnecessary words.

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 status/read-only tool with no parameters, the description fully covers what is returned: installed MCPs, connection status, accounts, and catalog tool counts. No output schema is present but the description makes the expected result clear.

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 flu. With no parameters to document, the description doesn't need to clarify them. The schema already covers everything (empty properties). Baseline 4 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 a specific verb ('Returns') and names the resource ('current toolkit state') with clear details: installed MCPs, connection status, accounts, and catalog tool counts. It clearly distinguishes from sibling tools like connect, authenticate, and show_version, 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 this tool is used to query the overall toolkit state but does not explicitly state when to use it over alternatives like 'show_version' or 'connect'. There's no exclusionary language or naming of when-not-to-use scenarios. The context is implicitly clear but not explicit.

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