SEFAZ SE: NFC-e

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

The description adds valuable context beyond annotations: explains the two invocation modes (with/without token), the permanent vs session distinction, and that the token is a JWT. Annotations already cover idempotency and non-destructive nature, and the description aligns without contradictions.

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 somewhat long but well-structured, leading with the purpose and then presenting clear options. Each sentence contributes useful information, with no fluff or repetition of schema details.

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 minimal schema and no output schema, the description covers all essential aspects: how to authenticate, the two usage modes, and the intended environment. It lacks explicit failure behavior but remains complete for typical 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 has only an optional 'token' with no description, so the description carries full burden. It explains that 'token' is a JWT for session-only login, and also covers the no-args call to obtain the link. This fully compensates for the 0% schema coverage.

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: authentication for MCP.AI in IDE agents. It specifies the exact actions (log in, get token, call with token or no args) and differentiates from siblings like 'connect' by focusing on token-based auth.

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 usage scenarios: permanent via config header, session-only via token, and no-args to get link. It doesn't directly compare with alternatives but gives sufficient guidance for correct invocation.

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 readOnly, idempotent, and non-destructive behavior. The description adds valuable return-state details: authenticated:true with empty pending[] when connected, and connect_url plus per-install URLs when credentials are missing. This exceeds the annotation-only information, though partial-connection states are not described.

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 sentences deliver meaningful, front-loaded information. The first sentence states the core purpose, and the second adds the key behavioral branches. No filler or repetition exists.

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?

There is no output schema, so the description must convey return behavior, and it does so for the primary scenarios. It is complete enough for a zero-parameter status tool, though a brief note about partial connection states would make it fully exhaustive.

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 there are no parameter semantics to document. The baseline of 4 applies because the description correctly focuses on output behavior instead of 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 and resource: 'Returns connection status and URLs.' It clearly distinguishes this from siblings like authenticate or show_version by focusing on connection state and URL output. The conditional details further clarify the exact 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 implies use for checking connection status and provides clear contextual conditions: all providers connected vs. credentials missing. It does not explicitly mention when not to use it or name alternatives, but the sibling list makes the purpose distinctive enough.

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?

Discloses behaviors beyond annotations: invoke works even when the MCP is not installed, may return connect links for auth, checkout links for payment, and writes require workspace owner/admin. It also explains that install makes MCPs permanent and adds tools to the toolkit. Annotations only provide basic hints (readOnlyHint:false, destructiveHint:false, openWorldHint:true), so the description adds substantial context about side effects and permissions.

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, extremely long paragraph (over 500 words) with no bullet points or section breaks. While it is packed with useful information, its lack of structure makes it difficult to parse quickly. It would benefit from a concise overview followed by action-specific details. This is not concise; it's overly dense.

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 complexity (23 parameters, multiple action types, no output schema), the description covers most critical aspects: search/describe/invoke flow, install vs invoke semantics, auth/payment handling, permission requirements, and the prompt library. It does not explain every parameter, but it gives enough context to understand the tool's full range. The lack of output schema is mitigated by describing what actions return (e.g., connect links, profiles).

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 carries the burden. It explains the core parameter 'action' with its enum values and describes the key parameters in context (mcp_id, tool_id, arguments, query, etc.) through the workflow. However, it does not systematically document all 23 parameters (e.g., limit, message, immediate, tier_slug, prompt_* fields are only mentioned within the prompt library section). Still, for typical use, the most relevant parameters are well explained.

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: 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It distinguishes itself from sibling tools (authenticate, connect, etc.) by focusing on catalog discovery and execution via actions. The verb 'run' and the resource 'marketplace' are specific and 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?

Provides explicit usage guidance: 'Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile ... invoke RUNS that tool.' It clearly differentiates when to use install vs invoke ('prefer invoke for a single/occasional use') and mentions alternatives like list_tools. Also specifies when written actions require admin privileges, and explains when to use the prompt library functions.

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 readOnly=false, non-destructive, and idempotent. The description adds that the conversation array is needed for reproduction, which is useful. However, it does not disclose what happens after submission, such as ticket creation, external transfer, or what response the agent should expect.

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 sentences with no filler. The first sentence states the tool's purpose; the second provides the key invocation guidance. Every clause 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 simple three-parameter tool with no output schema, the description is mostly adequate and should prevent major misuse. However, it omits any explanation of the optional 'context' parameter and gives no sense of the tool's return behavior or external side effects, so it is not fully 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?

With 0% schema description coverage, the description partially compensates by making clear that 'message' is the bug/feedback text and 'conversation' should contain recent messages. However, it never explains the 'context' parameter, and calling conversation an 'array' conflicts slightly with the schema's string type (default '[]').

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 concrete verb and resource scope: 'Report a bug, missing feature, or send feedback.' It clearly identifies this as the user-feedback tool, and none of the sibling tools (authenticate, connect, marketplace, etc.) overlap with that 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?

It provides clear context for when the tool should be used ('Report a bug, missing feature, or send feedback') and instructs the agent to include recent conversation messages for reproduction. It does not name explicit alternatives or exclusions, but no sibling tool appears to compete with this use case.

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, idempotent, and non-destructive. The description adds meaningful behavioral context beyond those flags: prepaid billing per consultation, absence of platform credential requirements, non-confidential government-source data, and LGPD data-controller responsibilities. This exceeds what annotations alone convey.

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 front-loaded with the core purpose, then adds relevant operational and legal details. It is a little dense but each sentence contributes useful context, and there is no redundant repetition of the tool name or annotations.

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 one-parameter, read-only tool, the description covers purpose, source, authentication, billing, and data sensitivity well. However, it does not describe the nfce parameter semantics or what the response contains, and with no output schema present, the agent is left with some uncertainty about invocation and return behavior.

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%, and there is only one required parameter, 'nfce'. The description refers to NFC-e in general but does not explain what the 'nfce' field should contain, its expected format, or any length/pattern constraints. This leaves a critical invocation detail underspecified.

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 identifies the action ('consulta') and the specific resource ('NFC-e' via SEFAZ SE), and it is easily distinguished from the sibling platform-management tools. The official-source framing reinforces a precise 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 gives clear operational context: it is hosted by the platform, requires no platform credentials, and is paid per query with prepaid credit. It does not explicitly name alternatives or exclusions, but none of the sibling tools overlap with this data-consultation 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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, signaling a safe, non-mutating operation. The description adds that it reports both platform and adapter versions, which is slightly more specific than the title. However, it doesn't disclose any other behavioral details like output format or potential side effects (e.g., network calls), but given the strong annotation coverage, the description adds marginal value 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 a single, concise sentence that is front-loaded with the key verb and resource. It contains no filler, and every word contributes to understanding the tool's purpose. This is an example of effective minimalism.

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 (zero parameters, no output schema, strong annotations), the description is complete enough. It tells the agent what information this tool provides (platform and adapter versions). It doesn't specify the return format, but that's not critical for such a simple query tool, and the annotations cover safety aspects.

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 schema coverage is 100% (vacuously). The description explains that it shows versions of the MCP platform and adapters, which gives context about what the output will contain. Since there are no parameters, the description doesn't need to compensate for parameter semantics, and it provides some useful information about the tool's focus.

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: showing current versions of the MCP platform and adapters. It is specific enough (verb 'show' + resource 'versions') and distinct from siblings like 'toolkit_info' or 'marketplace' that might expose different metadata. However, it doesn't explicitly contrast with siblings, so it falls short of a perfect score.

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 a simple use case: checking versions when needed. It doesn't explicitly state when to use this tool vs others like 'toolkit_info' or 'connect', nor does it mention any context such as troubleshooting or compatibility checks. The guidance is minimal but not misleading, so a baseline score is 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, side-effect-free operation. The description adds valuable detail about what data is returned (MCPs, status, accounts, catalog counts), going beyond what annotations specify. No contradictions exist between description and 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 sentence that leads with the verb and lists the returned components in a clear, scannable format. Every word adds value, with no fluff or repetition. It is concise yet comprehensive for a simple info-returning tool.

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 read-only tool with no parameters and no output schema, the description fully covers what the tool does and what it returns. The complexity is low, and the description addresses all relevant aspects, leaving no gaps in understanding. It is complete for an agent to know when and how to 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 tool has zero parameters, so the schema is empty and there is nothing to clarify. According to the rubric, a score of 4 is the baseline for 0-parameter tools, and no deduction is needed. The description effectively communicates that no input is required.

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'), then enumerates exactly what that includes: installed MCPs, connection status, accounts, and catalog tool counts. This distinguishes it from sibling tools like 'show_version' (which likely returns version info alone) and 'authenticate' (which handles auth flows).

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 this tool: whenever you need the current state of the toolkit. It does not explicitly mention alternatives or exclusions, but the context is self-evident given the tool's name and purpose. A small deduction for lacking an explicit 'use this instead of X' comparison, but not necessary for clarity.

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