SNCR: Consulta Pública de Imóveis

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

While annotations indicate the tool is not read-only and is idempotent, the description adds critical context: it guides the user to log in in the browser, copy the token, and explains the two authentication modes (permanent vs session-only). It does not contradict annotations and adds value by clarifying the interaction flow, though it could mention that calling with no args opens a link.

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 well-structured, with clear instructions separated by periods. It front-loads the key actions (log in, copy token) and then provides options for permanent and session login, making efficient use of words 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 tool has only one optional parameter and no output schema, the description covers the essential aspects: the purpose, authentication steps, token handling, and both usage modes. It is complete for an authentication tool, providing sufficient guidance 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 schema has one optional parameter 'token' with no description, and schema description coverage is 0%. The description compensates by explaining that the token is a JWT to be pasted, and that calling with no args gets the login link. This adds meaning beyond the raw schema, though it could be slightly more explicit about the format or usage.

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 purpose: authenticating an IDE agent to the MCP.AI server by logging in and obtaining an access token. It distinguishes itself from sibling tools by explaining the authentication flow, which is unique among the listed siblings.

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 explicitly explains two usage scenarios: permanent login via config header and session-only login by pasting the token. It also mentions when to call with no args to get the link, providing clear guidance on when to use this tool and how.

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 read-only, idempotent, non-destructive. The description adds value by detailing the response structure (authenticated, pending[], connect_url) and conditions, which goes beyond the structured annotations. 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 with the main function, and each sentence adds necessary conditional detail. 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?

While the description explains output for 'all providers connected' and 'credentials missing', it doesn't mention intermediate states (e.g., partial connectivity). Given no output schema, the description covers the primary cases but could be more exhaustive for a status 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?

Tool has zero parameters, so schema coverage is effectively 100%. The description doesn't need to explain params; baseline 4 applies. No added param semantics required.

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?

Clearly states the tool 'Returns connection status and URLs', with specific details about conditions (all providers connected vs. missing credentials). The verb 'Returns' and resource 'connection status and URLs' make the purpose unambiguous and distinguish it from siblings like 'authenticate' or 'marketplace'.

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, but the description implies this is for checking connection status and explains conditional outcomes. It does not say when to prefer this over authenticate or other sibling tools, but for a status-check tool the context is adequately 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?

With only sparse annotations (non-readonly, non-idempotent, open-world), the description carries the load and delivers rich side-effect disclosure: invoke 'runs the tool pontualmente (one-off), without adding the MCP to the toolkit', writes require 'workspace owner/admin', payment/auth produce 'a connect link' or 'checkout/top-up link' requiring user action, and publish_prompt 'returns a shareable mcp.ai/p/<slug> link that opens without login'. This fully reveals side effects and preconditions the schema and annotations omit. No contradiction with the openWorldHint:true annotation.

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?

Front-loaded with the identity and core flow in the first two sentences, then logically progresses through edge cases, install-vs-invoke, billing, permissions, and the prompt-library sub-domain. At ~360 words for a 23-param/14-action dispatch tool, the density earns most of its length, but multilingual artifacts ('consulta um CPF', 'pontualmente'), inline emphasis (KEY:, AND), and minor redundancy keep it from a 5.

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 tool of this complexity (23 params, 14 actions, dual marketplace+prompt-library domains, no output schema), the description covers the critical decision paths: search→describe→invoke flow, auth-required vs payment-required branches, admin permissions, install persistence, and the prompt library's shareable-link behavior. It stops short of documenting the `resume` action's semantics, exact output shapes, or rate limits, but the coverage is strong enough that an agent could safely and correctly drive most flows after reading it.

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 an above-average job on the critical `action` parameter, semantically unpacking most enum values (search, describe, invoke, install, list_tools, subscribe/cancel, report_bug, request_mcp, search_prompts, get_prompt, publish_prompt) and explaining the relationship between tool_id and invoke, plus prompt_slug semantics. However, several parameters remain unexplained (conversation, immediate, resume, message, arguments format), so it does not fully close the gap left by 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 opens with a specific verb+resource identity ('The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them') and immediately chains the core flow: 'action=search discovers MCPs by intent → describe returns one MCP's full profile... invoke RUNS that tool'. It clearly scopes the dispatch nature across 14 actions and distinguishes itself from siblings by owning the whole marketplace domain, while the two-verb contrast (invoke vs install) sharpens its boundaries.

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?

Exceptionally explicit when/why to use each action: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT in the active toolkit', 'request_mcp asks us to build a NEW MCP when nothing fits', and 'list_tools lists what is callable right now'. It even gives branch logic for auth/payment failures ('the user opens it, then you retry') and names the prompt-library as a separate sub-domain, which prevents cross-domain confusion.

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 readOnlyHint false, destructiveHint false, idempotentHint trueUTE. The description adds that the conversation array is needed for reproduction, which is behavioral context. It doesn't describe side effects (e.g., where the report goes), but for a feedback tool that's expected. With annotations covering basic behavior, this is acceptable.

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, zero fluff, front-loaded with purpose and includes key instruction. Very 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?

For a simple feedback tool, it provides the core purpose and one key instruction. It doesn't explain the 'context' parameter or anything about return behavior, but given no output schema and basic parameters, it's minimally complete. Could be improved by describing all 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 coverage is 0%. Description only mentions the conversation array and its purpose. It does not explain 'message' or 'context' parameters. The message is required so it's critical to describe, but absent. Thus the description fails to compensate for the lack of schema descriptions.

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's purpose: 'Report a bug, missing feature, or send feedback.' It uses a specific verb and resource, and distinguishes it from siblings like authenticate or marketplace. The scope is explicit, covering three related actions, and it immediately tells the user what to include (conversation array).

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 states when to use: for reporting bugs, missing features, or feedback. It implicitly says to include conversation for reproduction, which is a usage hint. However, it doesn't explicitly contrast with other tools or say when NOT to use it. Given siblings are mostly unrelated, this is adequate.

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, establishing the safety profile. The description adds that it covers 'MCP platform and adapter' versions, which is useful but does not describe return format or error behavior. This is comparable to the get_calls example, justifying a 3.

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 of 12 words, directly stating the tool's purpose. Every word earns its place with no fluff.

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, no-output-schema tool with strong annotations, the description fully covers the necessary context. The tool's simplicity means no further details are needed to ensure 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?

There are 0 parameters, and the schema coverage is trivially 100%. Per the rubric, 0 params sets a baseline of 4. The description adds no parameter-level detail, but none 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 a specific verb ('Show') and resource ('current MCP platform and adapter versions'), making its purpose unambiguous. It clearly distinguishes itself from sibling tools like 'toolkit_info' by focusing on version retrieval.

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 by the tool's simplicity, but there is no mention of when to choose it over siblings like 'toolkit_info' or 'authenticate'. This meets the 'implied usage' baseline.

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 the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses crucial behavioral traits: no platform credentials needed, pay-per-query with prepaid credit, non-confidential data, and LGPD data controller responsibility. This adds meaningful context about cost and legal obligations 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?

The description is three sentences long, each adding relevant information: purpose, hosting/billing, and data governance. It is somewhat dense but avoids fluff and is front-loaded with the primary action.

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 query tool with only two parameters and rich annotations, the description covers purpose, billing, official source nature, and legal responsibility. It does not describe return values, but this is not critical given the absence of an output schema and the simplicity of the query.

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%, and the description does not explain the parameters 'uf' and 'municipio' beyond their self-evident names. No format, examples, or required value formats are provided, leaving the agent to infer semantics from parameter names alone.

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 'Consulta Pública de Imóveis' (Public Property Consultation) and 'consulta em fonte oficial' (consultation from official source), identifying the action (consult) and resource (properties via SNCR). It distinguishes itself from sibling tools which are platform-level (authenticate, connect, 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 implies usage for querying official Brazilian public property data and mentions prepaid credit requirements, but it does not explicitly state when to prefer this tool over alternatives or provide exclusions. Usage context is implied rather than explicitly guided.

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 context about the returned data but doesn't disclose potential errors or side effects 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 a single, concise sentence that efficiently conveys all necessary 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?

The description explains what the tool returns, which is sufficient given no parameters and no output schema. It lacks mention of error conditions but remains complete for its intended purpose.

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 cannot add parameter meaning. Schema coverage is effectively complete (empty), warranting the baseline score.

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 with specific details (installed MCPs, connection status, accounts, catalog counts), making its purpose unambiguous and distinct from sibling tools.

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 mention when to use it over alternatives. However, given its read-only nature, it effectively serves as the tool for state queries.

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