SEFAZ MG: CADIN

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

With only sparse annotations, the description carries most of the behavioral burden. It discloses the two invocation modes (with/without token), the permanent vs session-only distinction, and that no args returns a link. It does not mention edge cases like invalid tokens or whether the token is stored server-side, but the main behavior is transparent.

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 covers the essential flow, but includes some conversational phrasing ('Best:', 'Or') and could be slightly tighter. Still, it is appropriately sized and front-loaded with the core login action.

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 no output schema, the description tells the user what to expect (a link for no-args, a set session for token input) and covers both configuration strategies. It is sufficient for an auth tool, though it could mention response values or failure behavior more explicitly.

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 only defines 'token' with no description, so the tool description adds substantial meaning: the token is a JWT from the browser, it is optional, it enables session-only login, and omitting it triggers link generation. This fully explains the parameter's role and 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 clearly states the tool's purpose: logging in via browser, copying an access token, and either persisting it as a header or using it for a session-only token. It conveys the auth flow specifically, but it does not explicitly differentiate itself from the sibling tool 'connect', so it misses full sibling differentiation.

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: use the permanent config-header approach for a non-expiring connection, or call with { token: '<jwt>' } for session-only login, or call with no args to get the link. It does not, however, state when to prefer this tool over sibling tools like 'connect'.

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 read-only, idempotent behavior. The description adds concrete details about return values and conditional outcomes (authenticated, pending, connect_url), which 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 concise, using two sentences to convey the essential information without any fluff or redundant wording. It is well-structured and 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?

Given that there is no output schema, the description fully explains the possible return states and their meanings, covering both success and failure cases. It is complete for the tool's purpose.

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 no parameters, so the schema coverage is complete. The description appropriately does not need to explain parameters, and the baseline score for zero parameters is applied.

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 returns connection status and URLs, which distinguishes it from sibling tools like 'authenticate' or 'marketplace'. It is specific enough to understand the tool's core 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?

It provides some context by describing behavior in different states (all providers connected vs. missing credentials), but does not explicitly state when to use this tool versus alternatives. The guidance is implied rather than explicit.

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 annotations by disclosing behavioral nuance: invoke works for not-installed MCPs, performs a one-off run, does not bloat the tool list, returns a connect link when credentials are missing, and can return a checkout/top-up link requiring a retry. It also states that write operations require workspace owner/admin and describes the installed_in_toolkit vs installed_in_workspace markers. This makes the tool's behavior highly predictable without contradicting any annotation.

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 text is lengthy, but the tool is unusually complex and the description is dense rather than redundant. It front-loads the core flow, then adds key caveats, access requirements, and the prompt-library sub-area. It could be improved with formatting/bullets, but every sentence provides functional decision-making context.

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 enormous surface area (10+ actions, 23 optional parameters, no output schema), the description covers a substantial amount of operational context: discovery vs. one-off vs. permanent use, auth/wallet edge cases, ownership/permissions, installed status flags, subscription controls, bug reporting, request_mcp, and the separate prompt library. Strongly complete for agent decision-making.

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 23 parameters and 0% schema coverage, the description carries a heavy load. It does explain the core action/mcp_id/tool_id/arguments flow and names the prompt-library operations, but it does not explicitly document many important parameters like limit, immediate, tier_slug, conversation, cancel_reason, disposition, or the prompt_* fields. The high-level semantics are clear, but individual parameter meaning is still partly inferred, so it only partially compensates for the absent schema descriptions.

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 identifies a specific resource (the mcp.ai marketplace) and a twofold purpose: to discover/catalog MCPs and to run/invoke them. It also names the individual sub-operations (search, describe, invoke, install, etc.), making it unmistakably different from a generic 'marketplace' label or the sibling tools.

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 is explicit about the intended usage hierarchy: use search to discover, describe to inspect, invoke for one-off runs, install only for persistent toolkit inclusion. It also actively distinguishes from alternatives where relevant ('prefer invoke for a single/occasional use', 'list_tools lists what is callable right now', 'request_mcp asks us to build a NEW MCP when nothing fits'). It gives excellent when-to-use vs. alternative guidance.

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 readOnlyHint=false and destructiveHint=false, but the description adds little beyond that. It does not disclose what happens after reporting (e.g., ticket creation, asynchronous submission) or any side effects. The only extra context is reproduction guidance, which is parameter-focused.

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 short sentences communicate the full purpose and essential guidance. There is no filler or redundancy, though the 'conversation array' phrasing could be tightened to match the schema's string type.

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 low-complexity reporting tool with one required parameter and no output schema, the description covers the core purpose and reproduction information. However, it omits what response to expect, how `context` is used, and misstates the type of `conversation`, leaving notable 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?

Schema description coverage is 0%, so the description must compensate. It explains the `conversation` parameter ('recent messages for reproduction') but incorrectly calls it an array when the schema declares a string. It leaves `context` completely unexplained and `message` is only implied by the main verb.

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 distinct resource types: bug, missing feature, or feedback. This clearly differentiates the tool from sibling tools like authenticate 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?

It states when to use the tool ('Report a bug, missing feature, or send feedback') and provides reproduction guidance ('Include the conversation array with recent messages'). It does not explicitly name alternatives or exclusions, 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?

The description adds valuable context beyond the annotations: it's hosted, requires no platform credentials, charges per query with prepaid credits, and the data is publicly available citizen info. It also discloses LGPD compliance responsibilities. While the readOnlyHint and idempotentHint annotations already indicate safety, the description enriches understanding by clarifying the data source is public information and the client is the data controller. This goes 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 moderately concise but tries to stuff a lot of legal and platform information into one sentence. While the content is valuable, the structure is a run-on with semicolons. The key operational details (what data, how it's queried) are present, but the legal and platform notes could be better organized. At 348 characters, it's on the longer side for what it communicates, but no sentence is wasted.

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 2-param, 0-required query tool with no output schema and no nested objects, the description covers the basic context: what it queries, how it's billed, and legal responsibilities. Given the tool's simplicity, the description is complete enough. However, information about expected input formats (masked CPF/CNPJ vs digits-only), rate limits, and the actual response format is missing. The description could also clarify whether a CPF is required for CNPJ queries or vice versa.

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 params documented in the description and 0% schema coverage, the description does nothing to explain the difference between 'cpf' and 'cnpj' parameters. The schema is minimal (just string types with no descriptions). The description's 'consulta' (query) and the tool name suggest these are identifier parameters, but no details are provided on format, validation, or which one to use. The agent has to infer semantics solely from parameter names and the LGPD context.

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 starts with 'SEFAZ MG: CADIN, consulta em fonte oficial' which clearly identifies the tool as a query against the official CADIN source for SEFAZ-MG. However, it doesn't explicitly state what 'consultar' returns or provide a clear verb+resource structure beyond the implicit 'query CADIN'. The description also mixes legal disclaimers and platform details, which dilutes the core purpose statement. It distinguishes itself somewhat from siblings by naming the specific government source, but the purpose could be clearer.

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: it's a hosted platform with prepaid credits, no platform credentials needed, and the data is the same as available to citizens (not confidential). It states the client is the data controller and responsible for legitimate purpose under LGPD. However, it doesn't explicitly say when to use this vs. alternatives or mention specific exclusions. The alternatives are not named, and there's no clear guidance on when not to use this tool. It provides context on pricing and legal compliance but lacks direct usage direction.

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 and destructiveHint: false, so the safety profile is covered. The description adds that it reports 'current' versions and names the exact items (platform and adapter), adding slight value beyond annotations but no deeper behavioral context like return format or side effects.

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 with zero waste. It is front-loaded and effectively communicates 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?

For a parameterless, read-only version display tool with no output schema and rich annotations, this one-line description is fully adequate. It covers what the tool does without unnecessary 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, so the baseline is 4. The description is not required to explain parameters and correctly focuses on the output purpose.

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 shows current MCP platform and adapter versions. It uses specific verb+resource ('Show' + 'MCP platform and adapter versions') and distinguishes from siblings by its unique focus 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 description implies the tool is for checking version details but doesn't explicitly state when to use it or mention alternatives. Context is clear but no exclusions or comparisons are 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful context by specifying exactly what state is returned (installed MCPs, connection status, accounts, catalog tool counts), which is useful beyond the annotations given the lack of an output 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 a single, well-structured sentence that front-loads the action and then lists the specific contents. Every word earns its place 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 no-parameter, read-only status tool with no output schema, the description fully explains what the tool returns and covers all relevant aspects. The annotations handle safety and idempotency, and the description provides the necessary output details, making the tool self-contained.

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 is an empty object with 100% coverage, so there is no parameter semantics burden on the description. Per baseline for 0-param tools, a score of 4 is appropriate.

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 clearly defines the resource ('current toolkit state') with enumerated content: installed MCPs, connection status, accounts, and catalog tool counts. This distinguishes it from siblings like show_version (which likely only reports version) and connect/authenticate (which modify 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 this is for inspecting toolkit state, but it does not explicitly state when to use this tool versus alternatives like show_version or connect. No exclusions or conditional guidance are provided, so usage context must be inferred.

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