SINTEGRA: MA

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

Annotations declare idempotentHint=true, destructiveHint=false, and readOnlyHint=false. The description adds behavioral value beyond these: it discloses the permanent non-expiring vs session-only distinction, what happens with no args (returns a link), and the token-as-JWT expectation. No contradiction exists between the 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?

All content is informative, but the description is a dense, run-on paragraph mixing config setup advice, security details, and invocation modes without structural breaks. It opens with context ('MCP.AI for IDE agents') rather than the core action, and would benefit from clearer partitioning of the config path versus the paste-token path.

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 single-optional-parameter auth tool with no output schema, the description covers the essential flow comprehensively: browser login, token retrieval, two successful login paths (config-based permanent, session-based), and the no-arg link mode. The moderate complexity is adequately addressed given the sparse annotations and schema.

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 full burden for the 'token' parameter. It compensates well by specifying the JWT format and clarifying that passing the token yields session-only login, while also documenting the no-arg alternative. This adds meaning the bare schema (a plain string property) does not convey.

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 verb (authenticate) and resource (MCP.AI for IDE agents), explaining the browser-login/copy-token flow. It is specific enough to distinguish the tool's intent, though it reads more like agent instructions than a crisp purpose statement, and it doesn't explicitly differentiate from the sibling 'connect' tool.

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 internal usage branches: calling with no args yields the auth link, calling with a token performs session-only login, and it recommends adding a header to config for a permanent non-expiring connection. This gives strong context on how and when to invoke each mode, though it offers no explicit exclusion guidance or contrast with 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 declare readOnlyHint=true and destructiveHint=false. The description adds valuable context about return behavior (e.g., 'returns authenticated:true and empty pending[]' when all connected, and 'connect_url' when credentials are missing) beyond what annotations convey. It contains no 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 concise, using just two sentences to convey the tool's purpose and key conditional behaviors. No filler words or 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 parameterless tool with no output schema, the description covers the main behavioral states (connected vs. missing credentials) and provides the relevant URLs. Though it could mention error cases or additional fields, the context provided is sufficient for an agent to understand the tool's output in most scenarios.

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 zero parameters and 100% schema description coverage, the baseline is 4. The description doesn't need to explain parameters since there are none, and it correctly focuses on response semantics.

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 'Returns connection status and URLs,' which is specific and distinguishes the tool from siblings like 'authenticate' by focusing on status rather than authentication actions. However, it does not explicitly contrast with any sibling, so it misses the full differentiation credit.

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 check connection status and get URLs) but never explicitly says 'use this when you need to...' nor mentions alternatives. The conditional response patterns hint at usage scenarios, but the guidance is not stated as direct instructions.

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 the generic annotations, the description reveals several important behaviors: invoke works even when the MCP is not installed and runs it one-off without bloating the toolkit; requires credential/login or payment, returning connect or checkout/top-up links; and that writes (install/uninstall/subscribe/cancel and the behind-the-scenes install from invoke) require workspace owner/admin. These details far exceed what readOnlyHint=false and openWorldHint=true convey, with no contradiction to 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 long but well-structured: definition → core flow → one-off execution behavior → install-vs-invoke guidance → permission requirements → prompt library addendum. Each section earns its place for a highly complex 23-parameter tool. Minor verbosity exists (e.g., the Spanish/Portuguese example "consulta um CPF" is illustrative but not essential), keeping it from a perfect score.

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 output schema and 23 parameters, the description is unusually complete. It explains the main outputs: describe returns a full MCP profile with tools/pricing/auth, invoke returns connect or checkout/top-up links when needed, get_prompt fills variables, publish_prompt returns a shareable mcp.ai/p/<slug> link, and search/describe flag installed status. It also covers permissions and the permanent vs. one-off distinction. Remaining gaps (e.g., exact shape of search results) are minor given the description's breadth.

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 across 23 parameters, the description carries the full burden. It provides rich meaning for the most critical parameter, action, by explaining every enum value (search, describe, install, invoke, list_tools, search_prompts, etc.), and covers mcp_id, tool_id, arguments, prompt_slug, prompt_vars. However, some parameters (limit, immediate, tier_slug, conversation, prompt_targets, prompt_category, cancel_reason, report_context) are left implicit and only inferable from context, so the compensation is good but not complete.

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 clear, specific definition: "The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them." It identifies the resource (marketplace) and its dual role as catalog and execution engine, then enumerates concrete capabilities (search, describe, invoke, install, prompt library). This clearly distinguishes it from sibling tools like authenticate, toolkit_info, 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 provides exemplary when-to-use guidance: it explicitly lays out the core flow "action=search discovers MCPs by intent → describe returns one MCP's full profile... → invoke RUNS that tool," and contrasts invoke vs. install: "Use install only to make an MCP PERMANENT in the active toolkit... prefer invoke for a single/occasional use." It also clarifies list_tools, request_mcp, and the prompt library functions, giving a complete decision framework.

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 the tool is idempotent and non-destructive. The description adds minimal behavioral detail, only mentioning the need to include conversation for reproduction. It does not elaborate on side effects or prerequisites, but it does not contradict 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 conveys the essential information without extraneous details. It efficiently captures the tool's purpose and key usage instruction.

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 adequately covers the tool's main function and the key input requirement (conversation). It does not discuss output or errors, but no output schema exists, so that is not a gap. Overall, it provides sufficient context 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 description explains the purpose of the 'conversation' parameter ('recent messages for reproduction') but does not clarify 'context' or 'message'. With only partial coverage of three parameters, the description adds some value but leaves other parameters unspecified.

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 distinguishes itself from sibling tools like 'authenticate' and 'show_version' by focusing on issue reporting.

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 implicit guidance on when to use the tool (when reporting a bug or providing feedback) and explicitly instructs to include the conversation array for reproduction. It does not explicitly mention alternatives, but the purpose is clear 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, establishing a safe, non-mutating operation. The description adds no further behavioral context (e.g., return format or errors), but it is consistent with the annotations and adequate for such a simple tool.

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 that directly conveys the tool's action. There is no extraneous content, making it highly 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's simplicity (no parameters, no output schema, no annotations beyond safety hints), the description sufficiently conveys its purpose. It could arguably specify the exact format of the version output, but for a version-check tool, the current level of detail is adequate.

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 coverage is 100% (an empty schema). Per the rubric, a baseline of 4 is appropriate when there are no parameters, and the description does not need to elaborate on parameter semantics.

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 states a specific verb ('Show') and resource ('current MCP platform and adapter versions'), making the tool's function clear. It does not explicitly differentiate from sibling tools like 'toolkit_info', but the purpose is unambiguous and self-contained.

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 no guidance on when to use this tool versus alternatives. It does not mention typical use cases (e.g., debugging, environment verification) or any conditions under which it should be invoked.

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, non-destructive behavior. The description adds valuable context: hosting by platform, no platform credentials needed, pay-per-query with prepaid credit, data is not confidential, and LGPD responsibilities. This goes beyond 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 a concise two-sentence paragraph that front-loads the primary purpose ('SINTEGRA: MA, consulta em fonte oficial') and adds critical context (payment, legal) without unnecessary detail. Every sentence provides essential 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?

For a low-complexity tool with no output schema, the description covers purpose, payment, and legal aspects adequately. However, it lacks any explanation of the query parameters, which are essential for correct usage, leaving the description incomplete for effective tool invocation.

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 three undocumented parameters (ie, cpf, cnpj) with 0% description coverage. The description does not explain their purpose, how to use them, or that they are alternative identifiers. With no parameter guidance in either the schema or description, the agent cannot know how to populate the query correctly.

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 ('consulta') of official SINTEGRA data for Maranhão ('MA'). It specifies the resource (official Brazilian sources) and distinguishes it from sibling tools like authenticate or report_bug, 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 usage context ('consulta em fonte oficial', 'disponível ao cidadão') but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or mention alternative tools for similar queries. Since sibling tools are platform-level rather than similar data lookup tools, the guidance is only implicit.

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 safety is clear. The description adds value by specifying exactly what state information is returned, giving the agent a concrete picture of the output 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 description is a single sentence that is densely informative yet concise. Each detail (installed MCPs, connection status, accounts, tool counts) contributes meaning, with no wasted words or repetition of schema 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?

For a simple read-only status tool with no parameters and no output schema, the description fully captures what the agent needs to know: it returns the toolkit state with specific components. There are no missing behavioral or return-value details that would impede correct invocation or result interpretation.

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 does not need to compensate for undocumented parameters; it focuses on the output, 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 uses a specific verb ('Returns') and clearly identifies the resource ('current toolkit state') with enumerated details (installed MCPs, connection status, accounts, catalog tool counts). This distinguishes it from sibling tools like 'show_version' or 'connect', which serve other 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 this tool is for checking toolkit status, but it does not explicitly state when to use it over siblings or when not to. No alternative tools are named, so the guidance is only 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.