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

Annotations already indicate idempotency and non-destructiveness, but the description adds valuable behavioral context: the distinction between permanent (config-based) and session-only (token-pasted) authentication, and the two invocation modes. It doesn't fully disclose response details, but it 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?

The description is three sentences, front-loaded with the tool's purpose and audience. Each sentence adds necessary information: purpose, recommended permanent setup, and alternative session setup. 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?

Given the single optional parameter, no output schema, and existing annotations, the description covers the main usage scenarios and flows. It lacks explicit return value details beyond the link for no-args calls, which is a minor gap but acceptable 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?

The schema has a single 'token' property with no description, and schema coverage is 0%. The description explicitly explains the token parameter as a JWT to be pasted, and also explains that omitting it triggers the link retrieval behavior. This fully compensates for the schema's lack of detail.

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 authenticates to MCP.AI for IDE agents, with a specific browser login flow and token handling. It distinguishes itself from sibling tools by detailing the authentication mechanism and use cases.

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 provides explicit when-to-use guidance: recommends adding the token to the server config for a permanent connection, or passing it to the tool for session-only login. It also clarifies that calling with no args returns the login link, giving the agent clear decision criteria.

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 and destructiveHint, but the description goes further by revealing the actual response structure (authenticated boolean, pending array, connect_url) and how it varies by connection state. This adds meaningful context beyond the annotations and does not contradict 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 two sentences, front-loaded with the core purpose ('Returns connection status and URLs') followed by conditional details. Every sentence provides distinct, essential information with 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?

For a zero-parameter, read-only tool with no output schema, the description adequately covers the primary behavior and return value structure. However, it only describes two states (all connected vs credentials missing) and does not mention partial connectivity or error cases, which could be a slight 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?

The tool has zero parameters, so the description does not need to explain parameter usage. Per the rubric, 0 params yields a baseline of 4. The description focuses on return values, which 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 clearly states the tool's function: 'Returns connection status and URLs.' It specifies the exact output in different scenarios (authenticated:true when all providers connected, connect_url and per-install URLs when credentials are missing), which distinguishes it from sibling tools like authenticate that likely perform authentication actions.

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 when to use the tool by outlining the response under different credential states, implying it is used to check connectivity status. However, it does not explicitly state when to use this over alternatives like authenticate, nor does it mention exclusions or prerequisites, 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 mark the tool as read-only and idempotent. The description adds useful behavioral context: no credentials, pay-per-query with prepaid credit, data is public and not private, and LGPD compliance. 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?

Three sentences with the main purpose front-loaded. No fluff, but the structure could be improved with clearer separation of purpose and constraints.

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 return fields (name, registration status, birth date) and key constraints, but it does not explain the 'completo' parameter or provide details on errors or cost structure. Since there is no output schema, more detail would be helpful.

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 0% description coverage. The description clarifies that CPF is the input but does not explain the 'completo' boolean parameter. This leaves a significant gap in parameter 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?

Clearly states the tool returns basic registration data for a person from CPF, listing specific fields (name, registration status, birth date). It is distinct from sibling tools, which are unrelated to CPF lookup.

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 clear usage context: requires prepaid credit, no credentials, and handles public data. However, it does not explicitly mention when to use this tool vs alternatives or list 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 the annotations by explaining the one-off behavior of invoke (works when MCP is not installed, returns connect/checkout links), the admin requirement for writes, and the distinction between toolkit/workspace installation. These are critical behavioral nuances not present in the structured metadata.

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 well-structured, with a clear opening, a core-flow section, a KEY note, and separate prompt-library coverage. It front-loads the most important information and avoids redundancy, though some sentences could be tightened to reduce overall verbosity.

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 23 parameters and no output schema, the description covers the primary workflows, conditional behaviors (credentials/payment), and permission constraints impressively well. It omits details on some secondary actions (e.g., resume, uninstall nuances) and does not explain every parameter, but the core usage is sufficiently specified.

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 zero schema_description_coverage, the description must compensate. It thoroughly explains the 'action' parameter by enumerating each action's purpose, and contextualizes tool_id, mcp_id, and prompt fields. However, many parameters (limit, immediate, conversation, tier_slug, cancel_reason, etc.) remain unexplained, leaving meaningful ambiguity despite the detailed narrative.

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 opening sentence clearly defines the tool as 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It then details the core flow (search → describe → invoke) and lists all sub-actions, making the purpose unmistakable and distinguishing it 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 explicitly advises when to use invoke vs install ('prefer invoke for a single/occasional use' and 'Use install only to make an MCP PERMANENT'), and distinguishes list_tools, subscribe/cancel, request_mcp, and the prompt library actions. It also notes that writes require owner/admin, which further guides appropriate use.

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=false and destructiveHint=false, indicating a non-read, non-destructive operation. The description adds that this is for reporting bugs/feedback and that the conversation array aids reproduction, but it does not disclose what happens after the report is sent (e.g., response format, tracking). 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, concise sentence that leads with the tool's purpose and includes a key usage note. No wasted words; every part contributes to understanding.

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 bug-reporting tool, the description covers the core purpose and the most relevant parameter (conversation). It lacks explanation of the 'context' parameter, but with annotations providing safety/effect hints and no output schema required, it is reasonably complete 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 has 0% description coverage, so the description must compensate. It explains the purpose of the conversation parameter (for reproduction), but it does not clarify the meaning of the required 'message' parameter or the optional 'context' parameter. The description implies message content but leaves ambiguity, and context is entirely unexplained.

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: 'Report a bug, missing feature, or send feedback.' It uses specific verbs (report, send) and identifies the resource (bug/feedback). This distinguishes it from sibling tools, none of which handle bug reporting.

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 how to use the tool: 'Include the conversation array with recent messages for reproduction.' It establishes the context for usage, but does not explicitly mention when not to use it or suggest alternatives. Since siblings are 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, so the safety profile is covered. The description adds a small clarification about which versions (MCP platform and adapters), but no additional behavioral context like output format or rate limits. This is adequate given the low complexity.

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?

Single sentence of eight words, front-loaded with the verb and resource. Every word 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 no-parameter, read-only version check with rich annotations, the description fully covers what the tool does. No output schema is needed to understand the return.

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; schema coverage is 100% vacuously. Baseline for 0 params is 4, and the description doesn't need to explain parameter semantics.

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' and identifies the exact resource ('current MCP platform and adapter versions'), which distinguishes it from sibling tools like authenticate and connect. No ambiguity.

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 via its clear purpose, but it doesn't explicitly state when to use vs alternatives, nor mention any exclusions. For a simple version check, the context is implied rather than spelled out.

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 value by detailing the exact state information returned (MCPs, status, accounts, tool counts), enriching the read-only context, though it doesn't mention return format or edge cases.

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 efficiently conveys the tool's purpose and output details with no wasted words. The description is front-loaded with the action verb 'Returns' and immediately specifies the resource and its components.

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 has no parameters and is a straightforward read-only state query. The description fully enumerates the output categories, making it complete for selection and invocation. No output schema exists, but the description sufficiently covers return content.

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 input schema has zero parameters, so no parameter explanation is needed. The description adds meaningful context about what state is returned without needing to compensate for schema gaps; baseline 4 is appropriate for no-parameter tools.

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, listing specific data: installed MCPs, connection status, connected accounts, and catalog tool counts. This specific verb+resource construction distinguishes it from sibling tools like authenticate, connect, and show_version.

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 used to inspect toolkit state, but provides no explicit guidance on when to use it versus alternatives. No exclusions or alternative tool mentions are given, so usage context is implied rather than explicit.

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