SEFAZ SP: Cupom Fiscal Eletrônico (SAT)

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

The description discloses that the tool initiates a browser login flow and returns a link when called with no args, and that it can accept a token for session-only login. It also mentions the permanent config option. Annotations indicate idempotentHint=true and destructiveHint=false, which align with the description. The description adds context about the browser interaction and token handling, though it doesn't detail what happens after token submission (e.g., validation, error handling).

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 concise and front-loaded with the tool's purpose. It uses a single paragraph with clear instructions, but it could be slightly more structured (e.g., separating the two usage modes). However, it is efficient and avoids 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?

Given the tool's simplicity (one optional param, no output schema), the description is fairly complete. It covers the main use cases and provides actionable steps. However, it doesn't mention potential errors, token expiration, or what the response looks like, which could be useful for an agent. But overall, it's adequate for the tool's complexity.

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 one optional parameter 'token' with no description, and schema description coverage is 0%. The description compensates by explaining that the token is a JWT to be pasted for session-only login, and that calling without args returns a link. This adds meaning beyond the bare schema, though it could be more explicit about the token format and 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 the tool's purpose: to authenticate the user for MCP.AI IDE agents by logging in via browser and obtaining an access token. It distinguishes itself from siblings by focusing on authentication, while siblings like 'connect' and 'marketplace' serve different functions.

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 usage scenarios: it explains when to use the tool (to log in and get a token), and offers two methods—permanent via config header or session-only by pasting the token. It also clarifies when to call with no args (to get the link) versus with a token (after user pastes). This is clear guidance on when and how to use it.

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?

With annotations already declaring readOnlyHint, idempotentHint, and destructiveHint, the description adds meaningful behavioral detail by explaining conditional outcomes: when all providers are connected, authenticated:true with empty pending[], and when credentials are missing, connect_url and per-install URLs. 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?

The description is only two sentences, fully front-loaded with the primary purpose, and every clause contributes meaningful information about return values and behaviors. There is 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 tool has no parameters, no output schema, and is a simple status/URL check, the description covers the essential scenarios effectively: the all-connected case and the missing-credentials case. It provides enough detail for an agent to correctly infer the tool's behavior and responses.

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 no input schema requirements, so the parameter dimension is trivially satisfied. The description correctly focuses on behavior rather than parameters, earning a baseline 4 for a parameterless 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 opens with a specific verb and resource: 'Returns connection status and URLs.' It clearly distinguishes this from siblings by focusing on status retrieval, while authenticate likely handles the actual connection flow.

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 wanting to check connection status and pending URLs—but it does not explicitly contrast it with alternatives like authenticate. The intended context is clear enough, but no exclusion guidance is provided.

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 well beyond the minimal annotations by revealing crucial runtime behaviors: invoke works even if the MCP is not installed, it does not bloat the toolkit, missing credentials produce a connect link, insufficient wallet funds produce a checkout/top-up link, and privileged mutations require owner/admin access. These are high-value behavioral disclosures that an agent needs to set expectations and decide the next action.

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?

This is a long but information-dense description with almost no wasted words; each sentence adds a distinct behavior or distinction. The main structural issue is that it is one dense paragraph rather than something more scannable with the input schema, but the density is justified by the breadth of the 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?

Given 23 parameters, no output schema, and no property descriptions, the text is commendably complete at the task level: it explains the core search-describe-invoke loop, auth and payment edge cases, the install-vs-invoke distinction, and the prompt-library sub-features. It still leaves some reachable actions and fields undocumented, such as the resume action and several prompt/unlikely parameters, so a fully frictionless invocation would still require some inference.

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 prose adds meaning for the main action-oriented fields, especially action values and tool_id, and explains the prompt, subscribe, and request flows. However, the 23-parameter schema has no property descriptions, and the description does not systematically map to the remaining fields such as arguments, immediate, tier_slug, conversation, resume, or cancel_reason. It gives enough for gross selection but not enough to confidently populate all parameters without 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 immediately identifies the tool as the official mcp.ai marketplace and explains that it is both a catalog of MCPs/tools and the execution layer for running them. It gives a concrete action flow (search → describe → invoke) and clearly distinguishes the tool from sibling utilities by defining its much broader catalog-execution scope.

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 routes when to use each action: search by intent, describe to inspect, invoke for one-off runs, install only for permanent toolkit additions, list_tools for callable tools, and request_mcp when nothing fits. It even calls out the key distinction between invoke and install with clear guidance to prefer invoke for single or occasional 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?

The description explains that it reports/sends feedback and notes the conversation payload needed for reproduction, which adds context beyond the annotations. However, it does not disclose what happens after reporting, whether any external destination is notified, or how idempotent/reporting behavior manifests.

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, direct sentences that immediately convey purpose and the one critical implementation detail. There is no filler, redundancy, or unnecessary wording.

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, this description gives enough to understand the purpose and the important conversation requirement. It lacks a little detail on the other two parameters, but the tool is simple enough that this description is close to sufficient.

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 burden is on the description to explain parameters. It meaningfully clarifies that 'conversation' should contain recent messages for reproduction, but it does not explain the required 'message' parameter's expected content or the optional 'context' parameter's role. This is partial compensation only.

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 action verb ('report') and clearly enumerates the supported intents: reporting a bug, missing feature, or sending feedback. This distinguishes it well from the unrelated sibling tools such as authenticate, marketplace, 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 clear context for when to use the tool and adds the important instruction to include the conversation array with recent messages for reproduction. It does not explicitly exclude alternative channels or name alternatives, but none of the sibling tools serve the same purpose, so this is sufficiently 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 mark the tool as read-only, idempotent, and non-destructive, and the description adds meaningful context beyond those annotations: no platform credentials are needed, billing is per-query prepaid, and the data is non-confidential and available to citizens. The LGPD note also clarifies data-handling responsibility.

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 purpose is front-loaded and the first two sentences are efficient. The later sentences about official data and LGPD responsibilities add context but are somewhat tangential to actually invoking the tool, making the description slightly less concise than it could be.

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 purpose and authentication/billing context are well covered. However, there is no output schema and the description does not describe the returned data or the expected 'chave' format, leaving some uncertainty for the agent about invocation and results.

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 a single 'chave' string with zero description coverage, and the description does not explain what 'chave' should be or its expected format. The meaning is inferable from the tool's domain, but the description adds no parameter-level value.

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 names the exact resource ('Cupom Fiscal Eletrônico (SAT)') and the action ('consulta em fonte oficial') with a specific scope (SEFAZ SP). This clearly differentiates it from the unrelated sibling tools about authentication, connectivity, and platform functions.

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: the tool is hosted by the platform, requires no platform credentials, and is paid per query with prepaid credit. It does not explicitly state when to prefer this over alternatives or provide exclusion conditions, so it falls short of full usage guidance, but the context is 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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds no additional behavioral context (e.g., return format, auth requirements). It neither contradicts annotations nor enriches them; it just restates the basic function.

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, focused sentence that immediately conveys the tool's purpose. No fluff or redundancy. It is appropriately sized for a simple version query 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 tool with no parameters, no output schema, and simple function, the description is sufficient to understand what it returns (platform and adapter versions). The lack of output structure detail is not a gap given the tool's simplicity and annotations.

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. The description correctly implies no arguments are needed. With zero parameters, a baseline of 4 is appropriate as per the rubric, and no further param explanation 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 clearly states the action ('Show') and the resource ('current MCP platform and adapter versions'). It is specific and distinct from sibling tools like 'toolkit_info' by focusing explicitly on version information.

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 purpose is self-evident from the name and description, so the agent will know when to use it. However, it does not explicitly mention when not to use it or differentiate from the sibling 'toolkit_info', which might also return version info. Clear context but no explicit 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds substantial detail about the return content (installed MCPs, connection status, accounts, catalog tool counts), which is particularly valuable since there is no output schema. This fully discloses the tool's informational behavior.

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 primary purpose ('Returns the current toolkit state') and then lists the specific details returned. Every word adds value, 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 read-only, parameterless tool, the description fully covers what the tool does and what it returns. The annotations handle safety, and the description enumerates the return items in sufficient detail even without an output schema. There are no gaps.

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 baseline is 4. No parameter documentation is needed, and the schema confirms an empty properties object. The description does not attempt to explain nonsensical params, so it correctly relies on the schema.

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 the current toolkit state' and enumerates specific data points (installed MCPs, connection status, accounts, catalog tool counts). This distinguishes it from sibling tools like authenticate or connect, which perform actions rather than reporting state.

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 (to inspect toolkit state) and there are no sibling tools that provide the same information. However, it does not explicitly state when not to use it or mention alternatives. Given the clear purpose and lack of overlapping siblings, a 4 is appropriate.

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