Similaridade de Identidade (Brasil)

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

Annotations already declare idempotent and non-destructive. The description adds valuable behavioral context: token-based session login vs. permanent header config, non-expiring vs. session-only, and the no-args behavior. 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 a single, information-dense paragraph covering all usage modes. It is somewhat verbose and has a minor grammar redundancy ('log in in'), but every sentence serves a purpose.

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 simple schema (one optional param) and no output schema, the description covers the full authentication workflow: browser login, token retrieval, config option, session option, and no-arg invocation. It is complete for an agent to use 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 0% description coverage, but the description fully explains the 'token' parameter as a JWT to paste for session login, and clarifies that omitting it returns a login link. This compensates completely for 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: logging in via browser and providing an access token. It identifies the specific resource ('MCP.AI for IDE agents') and the authentication action, distinguishing it from sibling tools like connect 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?

Explicit conditional guidance is given: the preferred permanent config approach vs. a session-only paste, and the no-args case for generating a login link. This tells the agent exactly when and how to invoke the 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, idempotentHint, and destructiveHint, so the safety profile is known. The description adds meaningful behavioral context beyond these annotations by detailing the two possible response states (authenticated with empty pending[] vs. missing credentials with connect_url), which helps the agent anticipate outcomes. This is valuable but not exhaustive, so a 4 is appropriate.

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 and front-loaded with the core purpose ('Returns connection status and URLs'). Every sentence adds needed information about state-dependent behavior, with no fluff or redundancy. It is appropriately concise and well-structured.

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: no parameters, no output schema, and safe read-only behavior. The description explains the two main scenarios and what fields to expect (authenticated, pending, connect_url). This is complete for the tool's scope, and sibling context doesn't require additional detail.

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 zero parameters, and the input schema is empty. According to the rubric, a zero-parameter tool receives a baseline of 4, as there is no parameter semantics to clarify. The description doesn't need to compensate for any parameter coverage 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 explicitly states the tool returns connection status and URLs, with a specific verb ('returns') and resource. It clearly distinguishes itself from sibling tools like 'authenticate' by describing a status check rather than an authentication action, and the conditional explanations further clarify 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?

The description provides clear context on when to use it by describing how it behaves when all providers are connected versus when credentials are missing. It implies a readiness check, though it does not explicitly name alternatives or exclusions, such as 'use authenticate for actual authentication.' This earns a 4 for clear context without 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?

The description goes far beyond annotations, explaining nuanced runtime behaviors: invoke is one-off and does not bloat the tool list, credential gaps produce connect links, wallet gaps produce checkout links and instruct to retry, install makes tools appear natively in future sessions, and search/describe surfaces installed_in_toolkit vs installed_in_workspace. It also notes that writes (install/uninstall/subscribe/cancel plus the one-off install behind invoke) require owner/admin. No contradictions 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 long but dense and highly organized: a summary sentence, core flow, key behavioral notes, action-by-action guidance, auth constraint, and prompt library subsection. Every sentence adds operational value; there is no padding or redundancy. For a tool with 14 actions and 23 parameters, the length is proportionate and the flow is logical.

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 is remarkably complete for a complex tool with no output schema. It covers all 14 actions, the request flow, fallback behaviors (connect/checkout links and retry), permission requirements, differentiation between invoke and install, and the separate prompt library. It even gives example capability queries ('find an MCP that does X', 'consulta um CPF'). An agent could select and invoke the correct action confidently based on this alone.

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 and 23 parameters, the description carries the full burden and succeeds. It explains the key action values and connects them to parameters: describe returns full profile with id+params+pricing+auth so you pick the right tool_id, invoke runs that tool, search discovers MCPs by intent, prompt actions handle prompt_text with {{variables}}. Even fields like prompt_slug and prompt_vars are contextualized via the shareable-link description. This adds meaning far 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 opens with a crisp definition: 'THE official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It clearly identifies the resource (marketplace) and verbs (search, describe, invoke, install, etc.), and distinguishes it from sibling tools like authenticate/connect by positioning it as the catalog/execution layer. The action list is explicitly enumerated, leaving no ambiguity about scope.

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 a full decision framework: core flow (search→describe→invoke), explicit when-to-use-invoke-vs-install ('invoke works even when the MCP is NOT installed' and 'prefer invoke for a single/occasional use' vs 'install only to make an MCP PERMANENT'), plus coverage of every sibling action (list_tools, subscribe/cancel, report_bug, request_mcp, prompt actions). It also states auth requirements for writes (requires workspace owner/admin), giving agents clear go/no-go conditions.

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 idempotentHint=true and destructiveHint=false, setting the safety profile. The description adds valuable behavioral context: it instructs the user to include the conversation array for reproduction, which is not evident from the annotations. No contradiction found.

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 concise sentences that deliver the purpose and key usage instruction without redundancy. Every word earns its place, and the extra instruction about conversation is directly relevant.

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 (3 params, no output schema, no nested objects) and the description covers the core action and the critical reproduction requirement. It could have elaborated on expected message content, but the description is sufficient for an agent to invoke 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 clarifies the 'conversation' parameter meaning (recent messages for reproduction), but 'message' and 'context' remain under-defined. The description adds partial value but does not fully explain all parameters, leaving some ambiguity.

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 ('Report') and clearly identifies the resource/action: bug, missing feature, or feedback. It distinguishes itself from all sibling tools (e.g., authenticate, connect) which serve 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 states when to use the tool (when reporting a bug, missing feature, or feedback) and provides specific guidance on including conversation messages for reproduction. It doesn't explicitly mention exclusions, but the context is clear and no alternative is needed given the sibling tools.

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=false, so the tool's non-mutating behavior is established. The description adds context by specifying that it returns 'current' platform and adapter versions, which aligns with the read-only nature.

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, focused sentence that front-loads the verb and object. Every word earns its place, with no redundant detail or padding.

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 parameters, no output schema, and a trivial operation, the description fully captures the tool's purpose and scope. Nothing else is needed.

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 need not explain any input semantics. The baseline of 4 applies as there is no additional burden for the description to compensate.

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 'Show' with a clear resource ('the current MCP platform and adapter versions'), leaving no ambiguity about the tool's function. It distinguishes itself from siblings like authenticate or marketplace by focusing solely on version information.

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 the tool is for retrieving version details when needed. Context is clear and self-contained, with no exclusions or alternatives mentioned, which is acceptable given the simplicity of the 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 provide readOnlyHint=true and idempotentHint=true, but the description goes beyond by disclosing payment mechanics (prepaid credit), data nature (public access, not private), and legal responsibility (LGPD, controller role). This adds meaningful behavioral context that is not available from the structured annotations alone.

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, with the main purpose stated upfront, followed by essential operational details (hosting, credentials, payment, data scope, LGPD). Every sentence adds value, and the structure is logical: function → access method → data nature → legal responsibility. No redundancy or 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?

Given the tool has 10 parameters, no output schema, and no parameter descriptions, the description does a good job of explaining the overall behavior and return value (similarity score). It also covers access, cost, and legal context. However, it does not explain the 'completo' parameter or provide details about error handling or response format, which would enhance completeness for an agent selecting this 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 schema has 0% description coverage, so the description must compensate. It names the key data fields: CPF, name, birth, address. This covers most of the 10 parameters implicitly (Address, City, State, PostCode fall under 'endereço'). However, the 'completo' boolean parameter is not explained, leaving a gap in understanding for a param that could alter the behavior of the query.

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: 'Compara os dados informados de uma pessoa no Brasil' and 'retorna o grau de similaridade com o cadastro.' The verb+resource structure is specific and distinguishes this tool from the sibling tools like authenticate, connect, or marketplace, which have completely 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 provides clear context for use: it mentions the tool is hosted by the platform, requires no credentials, and charges prepaid credit. It also clarifies that it accesses public data and addresses LGPD compliance. However, it does not explicitly state when not to use the tool or contrast it with alternative tools, but given the sibling list, no direct alternative exists.

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, and the description adds behavioral context by detailing exactly what state information is returned. It stops short of describing return format or potential latency, but for a read-only info 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 sentence that front-loads the core action ('Returns the current toolkit state') and then lists the specific items returned. Every word earns its place; there is no redundancy or 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?

This is a simple, no-parameter, read-only tool with no output schema. The description enumerates the return content sufficiently for an agent to know what to expect. Sibling tools are distinct enough that no additional exclusion context is required.

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 the schema trivially covers 100% of the parameter space. The description does not introduce any parameter confusion, and the baseline for zero-parameter tools is 4, which is appropriate here.

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 defines the resource ('current toolkit state') along with enumerated contents (installed MCPs, connection status, accounts, catalog tool counts). This distinguishes it from siblings like show_version and marketplace, which have different focuses.

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 for inspecting the overall toolkit state, but it does not explicitly state when to use it versus alternatives or provide any exclusions. Sibling differentiation relies on the name and enumerated contents rather than direct guidance.

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