SINTEGRA: ES

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

The description clearly discloses behavioral traits: it explains that using a header in config creates a 'permanent, non-expiring connection', while pasting a token gives 'session-only login'. It also implies side effects like storing tokens or modifying config, which goes beyond the annotations (readOnlyHint: false, idempotentHint: true). 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 concise, front-loaded with the main purpose, and every sentence adds meaningful information. It efficiently covers the primary workflow, configuration, and fallback without unnecessary fluff.

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 explains the two authentication modes and the token parameter, but it does not mention the output/return value of the call (e.g., what it returns after successful login) or error handling. However, given the absence of an output schema, it is reasonably complete for an authentication 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?

Even though the schema has 1 optional parameter with 0% description coverage, the description thoroughly explains the 'token' parameter: it's the JWT copied after browser login, used for session-only authentication. It also clarifies the no-arg call behavior (returns link). This fully compensates for the lack of 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 clearly states the tool's purpose: authenticate to MCP.AI by logging in via browser and obtaining an access tokenainer. It explains two distinct methods: permanent config header or session-only token. This is a specific verb (authenticate) with a clear resource (MCP.AI), and it distinguishes from sibling tools like 'connect' which might handle connections differently.

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 direct instructions on when to use each approach: 'Best: add it to this server's config...' for permanent access, or 'call with { token: ... }' for session-only. It also mentions calling with no args to get the link. However, it does not explicitly contrast with sibling tools like 'connect' or specify when authentication is required versus optional, leaving some ambiguity about 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 declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds valuable context about the two possible response states (authenticated with empty pending, or connect_url when credentials are missing), going 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 two concise sentences, front-loaded with the main action. Every clause adds value, and there is no redundant or filler content.

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?

Without an output schema, the description appropriately explains the tool's return behavior, covering both primary states. This is sufficient for the simple nature of the tool and its zero parameters.

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 schema coverage is effectively 100%. Per the rubric, a 0-parameter tool gets a baseline of 4, and the description adds no param-specific info because none exists, which 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 clearly states the tool's function: 'Returns connection status and URLs.' It differentiates itself from siblings like 'authenticate' by describing specific output states (authenticated:true vs connect_url), making it distinct.

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 used to check connection status, but does not explicitly state when to use it versus alternatives or when not to use it. There is no mention of exclusions or alternative tools, so it falls into the 'implied usage' category.

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 annotations (readOnlyHint=false, openWorldHint=true), the description reveals critical non-obvious behavior: 'invoke works even when the MCP is NOT installed — it runs the tool one-off without adding the MCP to the toolkit or bloating the tool list.' It discloses the auth redirect link, the wallet/checkout fallback, admin permission requirements for writes, and the no-login requirement for prompt links. This goes well beyond what the annotations 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?

Every sentence earns its place and the description is information-dense, but it is a single wall of uninterrupted prose with no paragraph breaks, lists, or visual anchors — a challenge for a tool with 14 actions and a prompt library. The numbered flow arrows (→) help, as does semicolon-delimited enumeration, but a long description of this scope would benefit from structuring. Adequate but not polished.

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 an extremely complex tool (14 actions, 23 params, no output schema), the description covers the core end-to-end flows (discover→describe→invoke), error/recovery paths (connect link, checkout link, retry), permission model, and even the return shape of publish_prompt (shareable mcp.ai/p/<slug> link). Minor gaps: no detail on search/list_tools result structures or subscribe tier semantics, but for the complexity involved this is notably 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 coverage and 23 params, the description carries full burden and largely delivers: it explains the action enum dispatch (all 14 values), mcp_id/tool_id/arguments flow, and the prompt_* param family. However, several params get no treatment (cancel_reason enum, conversation, request_*, report_context, query, limit, immediate, tier_slug), leaving the agent to infer their roles. Strong compensation for the core flow, incomplete on the long tail.

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+resource pattern: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It clearly differentiates from siblings by owning the full catalog domain (search/describe/install/invoke) versus siblings like authenticate, connect, and show_version. The catalog-vs-prompt-library duality is explicitly flagged, leaving no ambiguity about what this tool does.

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?

Pair-anchored justifications are explicit: 'Use install only to make an MCP PERMANENT' vs 'prefer invoke for a single/occasional use.' The description provides a full decision flow ('action=search discovers MCPs by intent → describe returns one MCP's full profile...'), states when invoke needs a retry ('the user opens it, then you retry'), and clarifies admin requirements for writes. This is exemplary when-to-use-vs-alternatives 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 indicate idempotentHint and non-destructive behavior, and the description adds context by explaining the inclusion of conversation for reproduction. It does not contradict the annotations and gives useful insight into expected input 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, consisting of two clear sentences. It efficiently conveys the purpose and a key usage hint without unnecessary elaboration.

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 gives enough context for a reporting tool, including what to report and the recommendation to provide conversation context. Since there is no output schema, it does not need to explain return values, and the description is reasonably complete for its intended function.

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 description explicitly mentions the conversation parameter ('Include the conversation array with recent messages for reproduction'), but does not explain the 'context' parameter at all and only implicitly suggests that 'message' contains the report content. With a low schema coverage, the description does not sufficiently compensate for the undocumented parameters.

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: 'Report a bug, missing feature, or send feedback.' It uses a specific verb and resource, and it is distinct from sibling tools like authenticate, connect, 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 indicates when to use the tool (for bug reports, feature requests, feedback) and provides guidance to include the conversation array for reproduction. It does not explicitly contrast with alternatives, but the sibling tools are unrelated, making the usage context 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 the tool as read-only and idempotent, so the safety profile is covered. The description adds valuable context by specifying that it reports both MCP platform and adapter versions, providing more detail than the annotations alone.

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, front-loaded sentence that is extremely concise and to the point. Every word earns its place, with no extraneous 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?

Given the tool's simplicity (no params, no output schema, read-only operation), the description is fully adequate. It tells the user exactly what the tool does, and the annotations cover the behavioral safety aspects, making the description complete for this context.

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. Per the guidelines, a baseline of 4 is appropriate when there are no parameters, and the description does not need to clarify any parameter semantics since none exist.

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 the specific verb 'Show' with the resource 'current MCP platform and adapter versions', clearly defining the tool's function. It distinguishes this from sibling tools like authenticate, connect, or report_bug, which have 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 the tool is for retrieving version information but does not explicitly state when to use it over alternatives like toolkit_info. There are no exclusions or clear contextual guidance, so it relies on the user inferring the obvious 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 declare readOnlyHint=true and destructiveHint=falsedurante. The description adds useful context: payment model (prepaid credits), lack of platform credentials, and LGPD compliance responsibility. However, it does not describe potential response format, pagination, or error behavior, so it's good but not exhaustive.

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, with a single paragraph covering purpose, authentication, payment, and legal note. It is front-loaded with the core query function. Slightly dense but acceptable.

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?

With no output schema and zero schema descriptions, the description provides minimal context about the tool's behavior. It explains the data source and payment but omits details about what data is returned, parameter usage, and any limitations. This leaves significant gaps for the agent to understand how to use the tool 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 has no descriptions (0% coverage), and the description does not explain the parameters 'ie' and 'cnpj' beyond their names. There is no elaboration on what each parameter does, whether they are mutually exclusive or optional, or any format requirements. This is a significant gap.

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 this tool performs a consultation/query of official Brazilian data sources (SINTEGRA ES). It names the resource (official sources) and the action (consulta). It distinguishes itself from siblings by being a data query tool, but doesn't explicitly contrast with alternatives. The description is specific enough but could be clearer about what data is returned.

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 context (official source, no platform credentials, paid per query with prepaid credits) but does not explicitly state when to use this tool versus alternatives, nor when not to use it. It implies usage for retrieving public official data, but lacks clear selection 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 cover read-only, idempotent, and non-destructive behavior. The description adds valuable context about the returned state (installed MCPs, statuses, accounts, catalog tool counts) without contradicting any 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 sentence, front-loaded with the main action and object, followed by a compact enumeration of details. Every word contributes value with no repetition 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 tool with no inputs and no output schema, the description fully specifies the content of the returned state. Combined with strong annotations, there are no significant gaps in understanding what this tool does or what it returns.

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?

This tool has zero parameters, so the schema contains nothing to elaborate. Per the rubric, a 0-parameter tool gets a baseline of 4. The description appropriately says nothing about parameters.

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 a precise resource ("current toolkit state"), then enumerates the exact contents: installed MCPs, connection status, connected accounts, and catalog tool counts. This makes it clearly distinct from siblings like show_version or authenticate.

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 establishes a clear context: this is the tool to inspect toolkit state. It does not explicitly state when not to use it or name alternatives, but the self-descriptive wording implies the usage scenario well enough, especially given sibling tool names.

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