Receita Federal Simei MEI: Emissão de Guia de Parcelamento

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

Annotations already signal idempotent, non-read-only, non-destructive behavior. The description adds valuable context beyond those annotations: permanent vs. session-only scope, the non-expiring nature of the config-header approach, and the behavior of calling with no args to retrieve a link. It does not cover error cases, but that is not a major gap given the simple optional-token schema.

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 and front-loads the primary action. Each sentence earns its place by covering a distinct mode: browser login/token acquisition, permanent config-header setup, and session-only token/no-args behavior. Minor informality in phrasing does not undermine clarity.

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 absence of an output schema, the description does enough to convey the invocation modes and expected outcomes: config-header login is permanent, token login is session-only, and no-args returns the link. It could describe the response shape after successful token submission, but the optional single-parameter interface is simple enough that the current guidance is sufficient for correct use.

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 no description for the optional token parameter, so the description carries the burden. It explains that token should be the JWT pasted by the user and that omitting it triggers the link-retrieval flow. This compensates well for the schema's 0% description coverage, though it could be slightly more explicit about the expected JWT format.

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: authenticate with MCP.AI by having the user log in through a browser and obtain an access token. It also explains the two delivery modes (permanent config header vs. session-only token parameter), which distinguishes it sharply from sibling tools like connect or 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 gives explicit usage guidance: for a permanent connection, configure the Authorization header; for session-only login, pass the pasted JWT as { token: "<jwt>" }; with no args, get the login link. It does not name sibling alternatives, but no other sibling serves an authentication role, so exclusions are not necessary.

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, idempotentHint, and destructiveHint=false. The description adds meaningful conditional behavior: authenticated:true with empty pending[] when connected, and connect_url/per-install URLs when credentials are missing. This does not contradict 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 two sentences, front-loaded with the core purpose and then conditional details. Every sentence earns its place with no 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 zero-parameter status tool, the description covers the main behavioral branches (connected vs. missing credentials) and mentions key output fields (authenticated, pending[], connect_url). It could specify the structure of per-install URLs or the pending array, but it is otherwise 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?

There are zero parameters, and schema coverage is trivially 100%. The baseline of 4 applies because no parameter documentation is needed, and the description focuses on return behavior.

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 a specific action and resource: 'Returns connection status and URLs.' This distinguishes it from siblings like authenticate (which likely performs authentication) and show_version (which reports version 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?

The description implies when to use the tool by explaining behavior when credentials are missing vs. all providers connected. However, it does not explicitly name alternatives or state when not to use it, despite the sibling 'authenticate' being a natural alternative.

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 (openWorldHint=true, readOnlyHint=false) match the description; no contradiction. The description adds significant context beyond annotations: invoke's one-off execution without toolkit bloat, the connect-link and checkout/top-up-link behaviors for auth/payment, and the owner/admin requirement for writes. It also discloses that search/describe flag installation status. Slightly docked because it omits details like pagination/limits on search and exact return shapes, but the permission and side-effect disclosures are strong.

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?

Every sentence earns its place — there's no fluff — but the description is a single dense ~230-word paragraph enumerating 14 actions. The crucial invoke-vs-install guidance ('KEY: invoke works even when the MCP is NOT installed') is buried mid-paragraph rather than lead or bulleted. For a tool this polymorphic, separating the MCP actions, the prompt-library actions, and the auth/billing notes into scannable segments would greatly improve usability.

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 23-parameter, multi-action gateway with no output schema, the description is remarkably thorough: it covers the search→describe→invoke pipeline, install vs invoke semantics, permission requirements, billing/auth behaviors, prompt-library flow, and even the mcp.ai/p/<slug> sharing behavior. Gaps: it never describes what search/describe/search_prompts actually return (no result-shape expectations) and the timeout/retry semantics for the described 'then you retry' flow are vague. Still, given zero schema descriptions, the coverage punches well above its weight.

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%, so the description must compensate — and it does for the core parameters: action (all 14 enums explained in prose), mcp_id, tool_id, arguments ('pick the right tool_id → invoke RUNS that tool'), and prompt_slug/prompt_vars (via the publish/get_prompt explanations). However, among 23 total parameters, several receive no semantics anywhere: cancel_reason, cancel_comment (enum has 8 values), tier_slug, immediate, request_name, request_details, report_context, conversation, prompt_category, prompt_targets, and prompt_description. A substantial tail of undocumented parameters remains.

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 establishes this as the mcp.ai marketplace gateway ('the in-platform catalog of every MCP/tool, AND the way to run them'), positioning it distinctly from workspace-level siblings like authenticate/connect. However, 'purpose' is inherently fragmented across 14 distinct action values (search, describe, invoke, install, publish_prompt, etc.), making the tool a kitchen-sink dispatcher rather than a single-verb resource. The core verb is ambiguous without the action parameter.

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?

Outstanding guidance: explicitly walks the core flow ('search discovers MCPs by intent → describe returns one MCP's full profile... → invoke RUNS that tool'), gives a clear decision rule between invoke vs install ('Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use'), and specifies when to use each auxiliary action (list_tools/report_bug/request_mcp/subscribe/cancel). It even explains the prompt-library vs MCP-tool distinction ('MCPs and the PROMPT LIBRARY... is about ready-made prompt TEXT rather than MCPs').

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 that the tool requires prepaid credit (pague por consulta), does not use platform credentials (sem credenciais da plataforma), and that the data is not confidential. It also states the user is the controller under LGPD. However, it does not explicitly mention read-only behavior or any side effects, though the annotation indicates read-only.

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 verbose and redundant, repeating legal disclaimers and promotional phrases. It could be significantly shortened without losing essential 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 lacks details about the output, parameter formats, or example usage. With no output schema and minimal parameter guidance, the tool is under-specified for a user to successfully invoke 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?

The parameters (cpf, cnpj, mes_ano, codigo_acesso) are listed without any explanation. The schema provides no descriptions, and the tool description adds no meaning to these fields, leaving their purpose ambiguous.

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: issuing installment payment guides (Emissão de Guia de Parcelamento) and querying official sources (consulta em fonte oficial) for Brazilian MEI data. It is distinguishable from siblings by its specific function.

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. It does not mention prerequisites, conditions, or scenarios where a different tool would be more appropriate.

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 communicate that the tool is not read-only, not destructive, and is idempotent. The description adds the useful detail that the conversation array is needed for reproduction, but it does not reveal what happens after submission, expected response, or side effects. This is adequate but not rich.

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 short, front-loaded sentences with no filler: it states the purpose and immediately gives the key usage instruction. 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?

Given the low complexity (3 simple parameters, no output schema, no nested objects), the description is largely sufficient: it covers the tool's purpose and the primary input guidance. The main gap is the unexplained 'context' parameter, but its default empty string makes it non-critical for 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?

Schema description coverage is 0%, so the description must compensate. It only clarifies the 'conversation' parameter as an array for reproduction, while the required 'message' parameter and the optional 'context' parameter are left unexplained. This is partial compensation and leaves meaning gaps for two of three 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 uses a specific verb 'Report' with clear resources: bugs, missing features, and feedback. It also names the key input needed (conversation array), and it is easily distinguished from the unrelated sibling tools like connect, marketplace, 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 clearly indicates when to use this tool: when there is a bug, missing feature, or feedback to send. It also gives actionable guidance to include recent conversation messages for reproduction. It does not explicitly state when-not-to-use or name alternatives, but no sibling tool competes with this purpose.

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, fully disclosing the safety profile. The description adds minor context by specifying both platform and adapter versions, but it doesn't provide further behavioral details beyond what the annotations already cover.

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, straightforward sentence with no filler or repetition. It is front-loaded and every word contributes to understanding the tool's function.

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's simplicity (no parameters, read-only, idempotent), the description is complete. It clearly states the output will include platform and adapter versions, and the annotations cover side effects and safety behavior, so no additional context is necessary.

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 there is nothing to explain. The baseline for a 0-parameter tool is 4, and the description appropriately avoids inventing parameter details.

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 a specific resource 'current MCP platform and adapter versions', clearly stating what the tool does. It does not explicitly contrast with sibling tools like toolkit_info, so while the purpose is clear, it doesn't actively distinguish itself from potential 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?

No guidance is provided about when to use this tool versus alternatives. The description only states what it does, leaving it to the agent to infer the appropriate context without any exclusions or comparisons to 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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds useful output detail but does not disclose extra behavioral traits such as whether this requires prior authentication, latency, or potential error conditions; this is adequate but not rich.

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 well-structured sentence that front-loads the core purpose ('Returns the current toolkit state') and then lists the specific output categories. Every clause adds useful information with no 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 that there is no output schema, the description carries the burden of explaining what the tool returns. It does so thoroughly by listing installed MCPs, connection status, connected accounts, and catalog tool counts, which is sufficient for an agent to decide to call it and interpret the result.

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 coverage is 100%, leaving no parameter ambiguity. Per the baseline for zero-parameter tools, a score of 4 is appropriate because there are no parameter semantics for the description to clarify.

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 resource ('current toolkit state') and enumerates the exact contents: installed MCPs, connection status, connected accounts, and catalog tool counts. This clearly distinguishes it from sibling tools like show_version or authenticate, 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 clearly positions the tool as a state/status introspection endpoint, useful when an agent needs to know what MCPs are installed and connected. It does not explicitly name alternatives or when-not-to-use cases, but the context is clear enough among siblings like connect, authenticate, and show_version.

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