Certidão TJ (Cível, Criminal e Fiscal)

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

Beyond annotations (idempotent, non-destructive), the description explains what happens: it provides a login link, accepts a JWT token for session, and distinguishes permanent vs session-based connections. It doesn't disclose side effects like invalidating previous credentials, but the behavior is well outlined.

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 but logically organized with a clear flow: purpose, best method, alternative method, and invocation forms. It's a bit verbose with phrases like 'MCP.AI for IDE agents' but every sentence provides useful guidance, avoiding 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 tool with no output schema and moderate complexity (two auth flows), the description covers the key aspects: how to initiate browser login, how to pass a token, and what no-args returns. It doesn't specify the response structure or error conditions, but it's sufficiently complete for an 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 has one optional 'token' string with 0% description coverage. The description compensates by explaining the token is a JWT for session login and clarifies that calling with no args returns the link. This adds meaning beyond the raw schema, though it could detail token format or required auth scopes.

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 this is for authentication: 'log in in the browser, copy the access token' and explains the two login modes. It distinguishes from sibling tools by focusing on IDE agent usage and token-based auth, which is a unique purpose among the 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?

Provides explicit when-to-use guidance: recommends adding token to server config for permanent connection, or pasting token for session-only login, or calling with no args to get a link. It doesn't explicitly compare to the sibling 'connect' tool, but the usage context is clear and actionable.

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, and the description adds valuable context by explaining the authenticated:true and pending[] fields, as well as connect_url behavior when credentials are missing. This goes 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?

Two sentences, with the main purpose in the first clause. No wasted words; every sentence earns its place by explaining different response states.

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 covers the two key states (connected and missing credentials) but could optionally detail partial connection states or the format of URLs. Still, for a simple status tool, it is reasonably 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?

The tool has zero parameters, so the baseline is 4. The description correctly focuses on the return behavior rather than 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 'Returns connection status and URLs' with a specific verb and resource, and elaborates on what is returned in different states. It distinguishes from sibling 'authenticate' by indicating this is a status read, not an authentication 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 usage for checking connection status, and the return behavior makes the context clear. However, it does not explicitly say when not to use it or mention alternatives like 'authenticate'.

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?

Discloses that invoke runs one-off without installing, returns connect links for auth needs, checkout links for empty wallet, and that writes require workspace owner/admin. This goes beyond the sparse annotations (readOnlyHint=false, openWorldHint=true) by clarifying side effects and permissions.

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 information-dense, organized around the core action flow, install vs invoke distinction, and prompt library. It is front-loaded with the essential purpose and each sentence adds useful guidance, though a more paragraph-structured layout would improve readability.

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?

Covers the full action surface: search, describe, invoke, install, list_tools, billing, bug reports, prompt publishing, and permissions. Despite no output schema, it explains expected result types (connect link, checkout link, slug link) and auth requirements, making it near-complete for a 23-parameter multi-action 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?

With 0% schema description coverage, the description compensates by explaining action semantics and key parameters (mcp_id, tool_id, arguments) through the core flow. It also contextualizes billing and prompt params via subscribe/cancel and publish_prompt, though not all 23 params are individually detailed.

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 it is the official mcp.ai marketplace — the in-platform catalog of every MCP/tool and the way to run them. Also covers capability requests and prompt library, distinguishing it from siblings like authenticate, connect, 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?

Explicitly instructs to prefer invoke for single/occasional use, install only for permanent addition, and list_tools for currently callable tools. Also explains when to use request_mcp, search_prompts, and report_bug, giving clear decision guidance.

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 the tool is non-destructive and idempotent. The description adds that the conversation array is used for reproduction, which provides useful context. However, it does not disclose additional behavioral traits such as response behavior 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 concise and front-loaded, stating the primary purpose in the first sentence and adding essential reproduction guidance in the second. No unnecessary words are used.

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 core purpose and highlights the key parameter for reproduction, but it omits the `context` parameter and does not clarify the conversation format. For a relatively simple tool, these gaps are noticeable but not fatal.

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 0% schema description coverage, the description must compensate. It explains the purpose of the `conversation` parameter but leaves `context` completely unaddressed and does not clarify the expected format of the conversation string. This partial coverage creates notable 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 sending feedback. It uses specific action verbs and identifies the resources, distinguishing it from unrelated 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 provides clear context on when to use the tool, listing specific scenarios (bug reports, feature requests, feedback). It doesn't mention exclusions or alternatives, but no sibling tool serves a similar function, so the guidance 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, so the safety profile is covered. The description adds the specific details that both MCP platform and adapter versions are included, giving useful 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?

One concise sentence, front-loaded with the action, 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?

For a simple version-info tool with no parameters and no output schema, the description fully covers the tool's purpose and scope.

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, so schema coverage is trivially complete. The description doesn't need to explain parameters, and the baseline of 4 applies.

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?

Clear verb 'show' with specific resource 'current MCP platform and adapter versions'. No sibling tool performs this function, so it's 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?

The description implies use when version information is needed. No alternatives exist among siblings (authenticate, connect, etc.), and no exclusions are necessary for such a simple read-only operation.

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 safety profile is covered. The description adds useful behavioral context: it is a paid operation consuming prepaid credit, queries public/official sources, and clarifies the data is not private. This goes beyond the annotations without contradicting them.

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 with no wasted words. The first sentence states the core purpose, the second adds cost and hosting details, and the third covers legal/compliance. Each sentence earns its place and is front-loaded with the most important 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 description provides substantial context (public data, payment, LGPD, official sources) but lacks details on the return format (e.g., PDF, JSON) and does not explain the many required identity fields. For an 11-parameter tool with no output schema and no parameter descriptions, this is a notable gap.

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 explains that CPF, CNPJ, and UF are key inputs ('a partir do CPF ou CNPJ e da UF'), but 10 of 11 parameters are required and undefined, including Nome, DataNascimento, NomeMae, NomePai, RG, Genero, and Tipo, plus the optional 'completo' boolean. Partial compensation only.

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 issues a civil, criminal, and fiscal certificate ('certidão cível, criminal e fiscal') for a person or company in a state Court of Justice, using CPF/CNPJ and UF. This specific verb+resource+scope distinguishes it from the generic sibling tools 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?

The description provides clear context: it is hosted on the platform, requires no credentials, and is paid per query with prepaid credit. It also notes that the data is public access and subject to LGPD compliance. However, it does not explicitly state when not to use it or mention alternatives, so it falls short of a 5.

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 read-only nature is known. The description adds value by specifying exactly what kind of state it returns (installed MCPs, connection status, accounts, tool counts), which is relevant behavioral 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 a single sentence that front-loads the core purpose ('Returns the current toolkit state') and then enumerates the contents in a compact, readable list. 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?

This is a simple state-inspection tool with no parameters and no output schema. The description sufficiently explains what the agent will receive (installed MCPs, connection status, accounts, and tool counts), making it complete for its complexity. The sibling context also confirms it is distinct from action-oriented tools.

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 parameter semantics are trivially satisfied. The baseline for 0 parameters is 4, and the description does not need to compensate for any schema 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 uses a specific verb 'Returns' and clearly identifies the resource as 'current toolkit state', then enumerates the exact components (installed MCPs, connection status, connected accounts, tool counts). This clearly distinguishes it from sibling tools like authenticate or connect, which are action-oriented.

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 clearly implies when to use this tool—whenever an agent needs to inspect the toolkit's state (installed MCPs, connections, accounts). However, it does not explicitly rule out alternatives or mention exclusions, so it stops short of full guidance.

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