NFe Completa

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

Annotations include idempotentHint=true and readOnlyHint=false. The description adds context beyond this: it explains the token is a JWT, the session vs permanent distinction, and the behavior when called with no args. This clarifies the tool's side effects and state implications without contradicting 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 dense sentences deliver all essential information: the purpose, the preferred config method, the session alternative, and the no-args case. There is no fluff; every clause adds value and the description is front-loaded with 'MCP.AI for IDE agents'.

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 one optional parameter and no output schema, the description covers the main usage scenarios (config, token, no-args). It doesn't discuss response formats or error handling, but that's not critical here; the tool's behavior is clearly outlined, making it usable in most contexts.

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 param (token) with no description (0% coverage). The description compensates by explaining that token is a JWT, how to use it (paste for session), and the no-args behavior. This gives the agent enough understanding to use the parameter 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 states the tool's purpose: to authenticate an IDE agent with MCP.AI. It specifies the resource (MCP.AI) and the action (login, retrieve token), and distinguishes from siblings by focusing on authentication flows. The verb 'authenticate' is reinforced with detailed steps.

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 usage context: it explains the recommended permanent approach (config header) versus session-based login (token argument), and notes that calling with no args returns a link. It does not explicitly compare with sibling tools, but the guidance within the tool is solid 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, and non-destructive behavior. The description adds valuable context on response states ('authenticated:true', 'pending[]' empty) and URL fields under different credential conditions. This goes beyond the structured annotations to inform the agent about expected outcomes.

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 every sentence adds useful information. 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 simplicity (no parameters, no output schema), the description covers the main return scenarios and key fields. However, it does not explicitly address partial connectivity states beyond 'all connected' and 'credentials missing', which leaves a small gap in completeness. Still, it is sufficient for most use cases.

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 is fully covered (100%). The description correctly does not add parameter details since none exist, and the baseline for zero-parameter tools is 4.

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 with a specific verb ('Returns') and resource ('connection status and URLs'), immediately distinguishing it from the sibling 'authenticate' tool. It also provides concrete output examples under different conditions, leaving no ambiguity about the tool's role.

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 usage is implied: you use this tool to check connection status or retrieve URLs for authentication. However, it does not explicitly state when to use this tool versus alternatives like 'authenticate' or when not to use it. The description offers clear context about behavior but lacks direct guidance on tool selection.

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?

Beyond the annotations (readOnlyHint=false, openWorldHint=true), the description discloses key behaviors: invoke runs one-off without installing or bloating the toolkit, returns connect/checkout links when credentials/wallet are missing, writes require workspace owner/admin, and search/describe flag installation status. It also details the prompt library's shareable-link behavior. This is rich contextual disclosure that goes well 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 a single dense paragraph of ~230 words, but every sentence carries meaningful information relevant to choosing and invoking actions. It is information-dense, though a bulleted or sectioned structure would improve scannability. No fluff or repetition is present.

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, the description partially explains return behaviors (e.g., invoke returns connect/checkout links, publish_prompt returns a shareable link, describe returns a full profile). However, it does not specify return formats for search, subscribe, uninstall, or report_bug, nor does it explain several actions' parameter requirements. Given the tool's complexity and 0% schema coverage, important gaps remain, but the high-level decision flow is well covered.

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%, and the description only partially compensates. It references `mcp_id`, `tool_id`, `arguments`, `query`, and all action enum values, but leaves many parameters undefined, such as `immediate`, `tier_slug`, `conversation`, `request_name`, `cancel_reason`, `prompt_body`, `prompt_vars`, `prompt_targets`, and `report_context`. An agent invoking actions with these parameters would lack guidance about their meaning or required 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 is explicit and specific: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It names the primary verbs (search, describe, invoke, install) and distinguishes the tool's role from siblings by covering the full lifecycle of MCP discovery, execution, and installation. It even identifies overlapping functionality with sibling tools (e.g., report_bug) while making its own broader scope clear.

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 explicit when-to-use guidance: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', 'request_mcp asks us to build a NEW MCP when nothing fits', and it explains the core flow search → describe → invoke. It distinguishes between similar actions (invoke vs install, search_prompts vs get_prompt) and gives conditional behavior for credentials/payment, which helps the agent select the right action.

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 adds significant behavioral context beyond the annotations: it discloses the hosting model ('Hospedado pela plataforma'), authentication requirements ('sem credenciais'), cost structure ('pague por consulta com crédito pré-pago'), data scope ('ACESSO PÚBLICO'), and legal responsibility (LGPD). These details help the agent understand operational and compliance aspects.

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, front-loads the core purpose, and includes necessary operational and legal context. It is slightly verbose with repeated emphasis on public access, but each sentence adds value and it remains easy 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?

The description covers key operational aspects: what it does, cost, auth, data privacy, and LGPD compliance. It does not describe return values, but there is no output schema and the tool is relatively simple. The annotations cover safety (read-only, idempotent), so the description adequately completes the picture for an agent to invoke the 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 must compensate. It clearly explains the 'Chave' parameter (access key) implicitly. However, the 'completo' boolean parameter is not explicitly described; its meaning is only inferable from the tool name and 'dados completos'. This partial compensation warrants a mid-level score.

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: 'Consulta os dados completos de uma Nota Fiscal Eletrônica (NFe) a partir da chave de acesso' – a specific verb ('Consulta'), resource ('NFe'), and method ('a partir da chave de acesso'). It distinguishes itself from sibling tools by being a data query operation, unlike auth, marketplace, etc.

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: for querying NFe data from an access key. It also notes important operational conditions (no credentials, prepaid credit) and the public nature of the data. It does not explicitly mention when not to use it or name alternatives, but the context is sufficient and there are no direct sibling 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?

Annotations already indicate non-readonly, non-destructive, and idempotent behavior. The description adds the context that conversation messages are needed for reproduction, which is helpful. However, it does not mention side effects such as where the report goes or whether a ticket is created.

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, front-loaded with the main purpose and no filler. Every word earns its place, and it is easily scannable.

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 reporting tool, the description covers the essential purpose and the critical input requirement (conversation). It does not discuss the optional context parameter or the return value, but given the lack of an output schema and the simplicity of the tool, this is acceptable.

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 descriptions are absent (0% coverage), so the description compensates partially. It explicitly explains the 'conversation' parameter as needing recent messages for reproduction. The 'message' parameter is self-explanatory, but 'context' is not mentioned. More detail would improve 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?

Description clearly states the tool's function: report a bug, missing feature, or feedback. The verb 'report' and resource (bug/feature/feedback) are specific and unambiguous. It also distinguishes from sibling tools like authenticate or show_version, which serve unrelated 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 implies when to use the tool: whenever there is a bug, feature request, or feedback. It does not explicitly mention alternatives, but none of the sibling tools are similar in function. It also provides a key usage instruction: include the conversation array for reproduction.

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 adds minimal behavioral detail beyond stating it shows 'current' versions, which is useful but does not elaborate on output format or side effects. It 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?

A single, front-loaded sentence with no redundant words. Perfectly 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?

For a zero-parameter version query, the description is fully sufficient. No output schema is needed, and the simple behavior is completely captured.

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 empty schema is fully described. The description needs no parameter details, so 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?

The description clearly specifies the action ('Show') and the resource ('current MCP platform and adapter versions'), making it distinct from sibling tools that handle authentication, marketplace, etc.

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 version info but does not explicitly state when to use it vs alternatives or provide context like 'use for debugging'. No exclusions or alternative tools are mentioned.

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 valuable behavioral context about the exact state information returned, which 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?

A single, well-structured sentence that lists the key return contents without fluff. 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 parameterless read-only state tool, the description fully covers what it does and what it returns. No output schema exists, so the enumerative details provide complete contextual information.

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 carries no parameter burden. Baseline for 0 params is 4, and no additional semantics are needed.

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, specific verb ('returns') and resource ('toolkit state') with detailed enumeration of what's included (installed MCPs, connection status, accounts, tool counts). This distinguishes it from siblings like show_version or connect.

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 communicates the tool's purpose as a state overview, giving the agent context for when to invoke it. It doesn't explicitly mention alternatives or exclusions, but the tool's uniqueness among siblings makes the use case evident.

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