Similaridade de Identidade (México)

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

Annotations already indicate idempotency and non-destructive behavior. The description adds useful behavioral context: that calling with no args returns a login link, while calling with a token establishes a session, and it distinguishes between permanent and session-only logging. 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 a single complex sentence but packs in essential instructions without fluff. It slightly suffers from being a wall of text with semicolons and parentheses, but every clause contributes meaning. Still, it's more structured than a terse one-word description.

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 optional parameter, no output schema), and the description covers the main workflows: config header for permanent auth, session token, and link retrieval. It doesn't specify error handling or exact response format, but for an authentication tool this is likely sufficient.

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 provides only a 'token' string with no description and zero coverage. The description fully compensates by explaining the token is a JWT copied from the browser, how to pass it, and that omitting it returns the login link. This is exactly what an agent needs to invoke the tool correctly.

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 specifies the tool's purpose: authenticating with MCP.AI for IDE agents, with a specific login flow (browser login, copy token, either configure a permanent header or provide a session token). It distinguishes itself from siblings by focusing on authentication rather than connection or marketplace operations, even though it doesn't explicitly name alternatives.

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 clear context for when to use the two modes: a permanent connection via config header versus a session-only login via pasted token. It also states that calling with no args returns a link. It doesn't explicitly mention when not to use this tool or compare it to sibling tools like 'connect', but the usage scenarios are well explained.

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, so the safety profile is established. The description adds valuable behavioral context by explaining the conditional returns (authenticated:true with empty pending[] vs. connect_url and per-install URLs), which goes beyond what annotations provide.

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-loads the primary purpose, and clearly explains both outcome scenarios. Every sentence contributes value without redundancy or unnecessary detail.

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 tool with clear annotations and no output schema, the description fully covers the necessary information. It explains what the tool returns in both success and incomplete credential states, making the behavior clear enough for an agent to invoke correctly and interpret results.

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 trivially covers them (100% coverage). The description adds meaning by explaining the output behavior based on credential availability, which compensates for the lack of parameters and enriches understanding.

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 'connection status and URLs', which is a specific verb+resource. It distinguishes from sibling tools like 'authenticate' by focusing on status rather than performing authentication, and explicitly describes the two possible outcomes.

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?

Usage is implied through the description of what the tool returns, but there is no explicit guidance on when to use this over alternatives like 'authenticate' or other informational tools. The context signals indicate sibling tools, but the description does not mention any exclusions or alternatives.

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 discloses key behavioral traits beyond annotations: invoke works without installing, returns a connect link when credentials are needed, and returns a checkout/top-up link for paid MCPs. It also notes that writes require admin/owner and that describe exposes pricing and auth info.

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, avoiding fluff and with each sentence delivering unique context. It lacks structured formatting (e.g., bullets), but the narrative flow is logical and front-loaded with the core 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?

For a tool with 14 actions, 23 parameters, and no output schema, the description provides substantial coverage of core flows, side effects, permissions, and the prompt library. However, it omits return-value details for most actions, though they are often inferable from the action semantics.

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 adds meaning for central parameters (action, query, mcp_id, tool_id, arguments, prompt_*) but leaves many others (limit, immediate, tier_slug, cancel_reason, cancel_comment, report_context, request_details) undocumented. It partially compensates for the overall 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 that marketplace is the official mcp.ai catalog, listing MCPs/tools and running them. It explains the core flow (search → describe → invoke) and distinguishes its capabilities from siblings like toolkit_info and report_bug.

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 guidance is provided: search for discovery, describe for full MCP profiles, invoke for one-off runs even when not installed, and install only for permanent additions. It also recommends invoke over install for occasional use and states that writes require workspace owner/admin.

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, and idempotentHint=true. The description adds no additional behavioral context beyond the conversation array hint, but it does not contradict the annotations. It neither discloses auth requirements nor what happens after the report is sent.

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 purpose, and every word earns its place. There is no fluff or redundant information, making it efficient for an agent to parse.

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 no output schema, the purpose is clear and the conversation hint is useful, but the description omits explanations for the other parameters and does not describe what the agent should expect in response. It is adequate but has clear gaps.

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 only mentions the conversation array and does not explain the required 'message' parameter or the optional 'context' parameter. This leaves the agent to infer that 'message' contains the report text and 'context' is supplementary, which is not explicit enough.

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 'Report a bug, missing feature, or send feedback' with a specific verb and resource. This distinguishes it from sibling tools like authenticate, connect, and show_version, which serve different functions.

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 a specific instruction to include the conversation array for reproduction, which is action-oriented, but it does not explicitly state when to use the tool versus alternatives or any exclusions. Usage is largely implied: use it when you want to report an issue.

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, which cover the safety profile. The description does not add extra behavioral context beyond what annotations provide, but it is consistent with them. The tool's simplicity mitigates the lack of additional detail.

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 directly states the tool's purpose. It is front-loaded with the action ('Show') and the target ('current MCP platform and adapter versions'), with 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?

The tool is very simple with no parameters, a clear read-only annotation set, and no output schema. The description fully captures what the tool does and what the agent needs to know to invoke it correctly. There are no missing pieces of context.

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, making parameter semantics trivially clear. The baseline for 0 parameters is 4, and the description does not need to add further parameter details. It fully aligns with the schema, which has no properties.

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: to show current MCP platform and adapter versions. It uses a specific verb ('Show') and resource, making its purpose evident. It does not explicitly contrast with sibling tools, but its uniqueness is implied since no sibling shares a similar 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 no guidance on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions. The agent is left to infer usage from the tool's name and description alone.

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. The description goes beyond this by disclosing the payment model ('pague por consulta com crédito pré-pago'), the public nature of the data ('ACESSO PÚBLICO em bases e fontes oficiais'), and LGPD compliance. It does not explain error handling or exact response format, but the annotations cover the main safety profile.

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 value: the purpose, the access/payment model, and a legal note. It is front-loaded with the core function and contains no irrelevant filler. Every phrase earns its place.

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 complex tool with 10 parameters, no output schema, and no parameter descriptions, the description covers the high-level purpose, cost, and data nature, but leaves gaps: it does not specify the output format (e.g., range of similarity scores), the behavior of the 'completo' parameter, or potential error scenarios. It is adequate for basic selection but not for full autonomous 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?

Schema description coverage is 0%, with 10 parameters and no descriptions in the schema. The description mentions 'CURP, nome, nascimento, endereço', which loosely maps to several parameters, but it does not explain the 'completo' boolean, required formatting (e.g., CURP structure), or the meaning of each field. This is insufficient compensation for the complete lack of schema-level documentation.

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 México (CURP, nome, nascimento, endereço) e retorna o grau de similaridade com o cadastro.' This specifies the verb (compares), the resource (person data in Mexico), and the output (similarity degree). It also distinguishes itself from the unrelated sibling tools (authenticate, connect, etc.) by being a data validation 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?

Provides useful context: hosted by the platform, no credentials required, pay-per-query with prepaid credit, and it queries public-access information. This implies when to use it (for identity/similarity checks using public data) and highlights its paid nature. However, it does not explicitly state when not to use it or compare with alternative tools, though no comparable siblings exist.

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, establishing a safe read operation. The description adds meaningful detail about what state is returned, such as installed MCPs and account connections, which helps the agent understand the tool's output beyond the safety profile.

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 entire description is a single sentence that front-loads the verb and immediately lists the returned state components. No wasteful words or redundancy with the schema.

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 parameterless read-only tool with no output schema, the description fully specifies what the agent will receive: installed MCPs, status, accounts, and tool counts. Nothing essential is missing given the tool's simplicity.

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 provides complete coverage with an empty object. Baseline per rules is 4; the description doesn't need to add parameter semantics, and it doesn't, keeping the focus on return contents.

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 'Returns' and names the resource 'current toolkit state', enumerating installed MCPs, connection status, accounts, and catalog tool counts. This clearly distinguishes it from sibling tools like connect and authenticate which perform actions rather than report state.

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 is for inspecting toolkit state when needed, listing exactly what information is included. It doesn't explicitly mention alternatives, but for a zero-parameter info tool, the context is clear and no competing sibling serves the same purpose.

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