SEFAZ CE: NFC-e

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

Annotations (idempotentHint=true, destructiveHint=false, readOnlyHint=false) are not contradicted. The description adds value by revealing that calling with no args returns a linkásession flow, and that providing a token completes authentication. It also suggests a permanent header configuration, adding context beyond the 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 moderately long but well-structured: starts with context and audience, then explains two invocation patterns. Each sentence adds value; no filler. Slightly verbose but acceptable for an auth tool needing 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 low complexity (one optional parameter, no nested objects, no output schema), the description covers essential behavioral aspects: login flow, token usage, permanent vs session, and return behavior (link). It could mention the exact response format, but it's sufficient for an agent to understand and call 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?

The schema provides only the parameter name 'token' with no description (0% coverage). The description compensates fully by explaining that token is a JWT, the exact format '{ token: "<jwt>" }', and that omitting it triggers returning a login link. This gives complete meaning to the parameter.

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: 'log in in the browser, copy the access token' for MCP.AI IDE agents. It distinguishes from siblings by focusing on authentication setup, and explicitly explains the token flow and alternative configuration (header vs session token).

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 guidance on two usage modes: 'Best: add it to this server's config as a header...' and 'Or paste it here for a session-only login.' It also explains when to call with no args ('no args to get the link'). While it doesn't explicitly mention alternatives among siblings, it gives actionable instructions on how to use the tool in different scenarios.

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 valuable behavioral context beyond the annotations (readOnlyHint, idempotentHint, destructiveHint) by explaining the exact response in different states, including the presence of 'authenticated', 'pending[]', and 'connect_url'. This is more than what annotations alone 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-loaded with the core purpose, and every sentence adds useful detail without unnecessary words. It is 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?

Given the tool has no parameters and no output schema, the description fully covers the important scenarios: all connected, and missing credentials. It provides enough information for an agent to decide when to call and what to expect, especially with the sibling tool list providing context for alternatives.

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 covers 100% of parameter information. The description correctly focuses on the tool's behavior rather than parameters, matching the baseline for a no-parameter tool.

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 tool's function with a specific verb ('Returns') and resource ('connection status and URLs'), and distinguishes it from sibling 'authenticate' by describing what it returns under different conditions, not how to initiate authentication.

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 call this tool (to check connection status and get URLs), but it does not explicitly state when not to use it or name alternatives. The implied alternative is 'authenticate', but it is not directly referenced.

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 behavioral traits such as: invoke works without installing, may return connect/checkout links if credentials/wallet needed, writes require workspace owner/admin. This adds context beyond annotations, which only say non-readOnly, openWorld, non-idempotent, non-destructive. Minor gaps: no mention of rate limits or error handling, but strong coverage.

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 but highly structured, with clear flow: search → describe → invoke, plus additional actions and prompt library. It uses 'KEY:' to highlight critical info, making it readable despite length. Every sentence adds value.

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 high complexity (14 actions, 23 params, no output schema), the description covers all major flows, parameter purposes, auth requirements, and billing nuances. It is complete enough for an agent to select the right action and 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 description coverage is 0%, so the description must compensate. It explains key parameters like action, mcp_id, tool_id, arguments, and prompt-related params (prompt_body, prompt_slug, prompt_vars). However, it does not detail all 23 parameters (e.g., limit, query, conversation), but the core usage semantics are covered well.

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 the tool is the official mcp.ai marketplace and covers both MCP discovery and execution. It distinguishes from siblings by mentioning the prompt library and specific actions like invoke and install, which are not in sibling names.

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 guidance on when to use each action: use invoke for one-off runs, install for permanent toolkit additions, search for discovery, and describes the flow. It also differentiates from siblings like authenticate and connect, making alternatives clear.

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, destructiveHint=false, and idempotentHint=true. The description adds useful reproduction context by asking for the conversation array, but it does not disclose side effects, destination of the report, or response behavior. 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?

The description is two sentences, front-loaded with the core purpose, and contains no filler or redundant 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 tool is simple with 3 parameters and no output schema, and the description covers the core purpose plus one usage detail. However, it omits semantics for message/context and does not clarify what happens after submission. There is also slight tension: conversation is optional in the schema but the description instructs to include 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?

Schema description coverage is 0%, so the description must compensate, but it only mentions the conversation parameter. It does not explain the required 'message' parameter or the optional 'context' parameter, leaving their semantics to inference.

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') and names concrete resources: bug, missing feature, and feedback. This clearly distinguishes the tool from siblings like authenticate, connect, and marketplace.

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 tool: reporting bugs, missing features, or feedback. It also provides a concrete usage instruction to include the conversation array for reproduction, though it does not explicitly name alternatives or 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?

Annotations already mark the operation as read-only and idempotent, so the description adds useful behavioral context: prepaid per-query cost, no credential requirement, public data availability, and LGPD responsibility. This goes beyond the annotations without contradicting 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?

Four sentences convey purpose, source, cost, credentials, data sensitivity, and legal responsibility without padding. Each sentence contributes new context, though it is slightly wordier than a minimal high-value 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?

For a one-parameter, no-output-schema consultation tool, the description covers official provenance, pricing, credentials, information availability, and legal responsibility. It could be improved by clarifying the expected response shape or the exact input key format, but overall it is reasonably 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?

The input schema exposes a single string 'nfce' with 0% description coverage. The description mentions 'NFC-e' as the resource, but it does not explain the expected identifier format (e.g., access key length or encoding), which the agent needs to interact reliably.

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 performs a consultation of NFC-e documents against the official SEFAZ CE source ('SEFAZ CE: NFC-e, consulta em fonte oficial'). It also distinguishes itself from the unrelated platform sibling tools by focusing on official Brazilian data retrieval.

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 context: use it when you need official NFC-e data, and mentions important conditions like prepaid credit and no platform credentials. However, it does not explicitly state when to avoid using it, nor does it name or compare against any alternative lookup tool.

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 the specific output (platform and adapter versions) without contradicting any annotations. It does not describe side effects, but none are expected given 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, clear sentence with no fluff. It is appropriately concise and well-structured, conveying the essential information without 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 tool with no parameters and no output schema, the description sufficiently communicates what the tool returns (version info). It does not detail the format, but that is not critical for understanding the tool's purpose. The description is complete enough for a simple read-only 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 tool has no parameters (0 params), so the baseline of 4 is appropriate. The description correctly omits any parameter details, as none exist, and the input schema is empty.

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 the current MCP platform and adapter versions. It is specific and distinct from sibling tools like authenticate, connect, or marketplace, 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 implies when to use the tool (when version information is needed) but does not explicitly compare with alternatives or provide guidance on when not to use it. This is acceptable for a simple read-only tool, but explicit usage context is missing.

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 and idempotentHint=true, and the description aligns with those. The description adds useful context by detailing what the returned state covers (MCPs, connections, accounts, catalog tool counts), going beyond the structured metadata without contradicting it.

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 concise sentence explains the function and listing of all outputs without filler, repetition, or irrelevant details. It is well-formed and easy to parse for an AI agent.

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 info tool, the description fully covers the necessary return content: installed MCPs, status, associated accounts, and tool counts. No output schema is needed because the description sufficiently enumerates the principal results, and the low complexity does not require further detail.

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 100% schema coverage (the schema is empty), so there is nothing the description needs to explain. The baseline 4 is appropriate because no additional territory is missing.

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 highly specific: it states 'Returns the current toolkit state' and enumerates exactly what that includes (installed MCPs, connection status, accounts, and catalog-tool counts). This clearly distinguishes it from action-oriented siblings like connect, authenticate, 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?

The description implies use when you need a snapshot of the current toolkit state, but it does not explicitly say when to use this tool versus alternatives or when not to use it. For example, it doesn't note that show_version might be the better choice for version-only information.

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