SINTEGRA: TO

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

Beyond the annotations (idempotent, non-destructive), the description discloses key behavioral details: token is a JWT, the config-based method yields a non-expiring connection, and calling with no args returns a link. While it doesn't describe failure modes or the exact return value, it adds meaningful context about the authentication flow.

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 slightly verbose with an introductory audience note ('MCP.AI for IDE agents'), but every subsequent sentence conveys necessary information. It is front-loaded and organized, with clear separation of the two authentication modes. Overall, it is appropriately sized for the complexity.

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 is complete enough for a simple tool: it explains the two workflows, the token's role, and the no-args link retrieval. However, it does not explicitly state what the function returns after successful authentication or any errors that might occur, which would be helpful given there is no output 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?

The only parameter 'token' is clearly explained: it is the JWT pasted by the user for session-only login. The description also explains the no-args behavior. Since schema coverage is 0%, this description fully compensates by giving the parameter meaningful context and format.

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: authenticating users via browser login and token exchange. It uses specific verbs like 'log in' and 'copy the access token,' and distinguishes itself from sibling tools like 'connect' or 'marketplace' by focusing on the authentication 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 provides explicit usage guidance: it explains when to call with no args (to get the login link), when to pass a token (session-only login), and recommends configuring the token in the server config for permanent access. This directly addresses when and how to use the tool versus 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 establish readOnlyHint, idempotentHint, and destructiveHint. The description adds meaningful context by specifying the two outcome states: authenticated:true with empty pending[], and missing credentials with connect_url and per-install URLs. 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 two short sentences. The first sentence front-loads the core purpose, and the second efficiently explains the two conditional response states. There is no wasted 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 zero-parameter, read-only status tool with no output schema, the description names the key response fields (authenticated, pending[], connect_url, per-install URLs) and covers the main conditions. It could be slightly more explicit about what 'pending' contains, but it is largely complete for the tool's simplicity.

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 schema is empty and schema coverage is 100%, so no additional parameter semantics are needed in the description.

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 a specific verb ('Returns') and resource ('connection status and URLs'). It also distinguishes itself from the authenticate sibling by describing a status/read operation rather than an authentication action.

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?

No guidance is given about when to use this tool versus alternatives like authenticate. The description explains conditional output states, but that is behavioral context, not usage guidance 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 only provide generic flags (readOnlyHint:false, openWorldHint:true), so the description carries the burden and handles it excellently. It discloses side effects (invoke may not require installation; one-off mode; auth/checkout links), auth requirements (workspace owner/admin for writes), and persistence semantics (installed_in_toolkit vs installed_in_workspace; install makes things permanent; prompt links open without login). No contradiction with annotations; annotation_contradiction is false.

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 dense but well-structured paragraph that uses imperative phrasing and lead-ins like 'Core flow:', 'KEY:', 'Search/describe flag...' and 'Writes...' It front-loads the core purpose and creates a clear mental model, but it is a long run-on paragraph and could benefit from breathable structure (bullets or separators). Still, each clause earns its place.

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 complex facade with 23 parameters, 14 actions, no output schema, no required params, and minimal annotations, the description covers the main flows, permission requirements, auth/payment retry behavior, and fallback paths (report_bug, request_mcp, prompt library). It appropriately describes the output in terms of flows rather than return values, and given the absence of an output schema, that is acceptable. It could mention what 'resume' does, but overall this is a complete picture for an agent to act.

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 carries the burden, but the schema is action-dispatch with 23 parameters, most empty-string defaults. The description explains the high-level parameter semantics (action=search, describe, invoke, subscribe/cancel, prompt-related actions) and names key concepts like prompt variables. It does not enumerate each parameter individually, but given the size of the schema and the action-switch nature, naming all actions and core flow is strong compensation. Losing one point for not covering obvious details like the 'resume' action and parameters like limit/query/immediate.

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 explains this is a multi-action facade for the mcp.ai marketplace covering catalog search/discovery, tool invocation, installation, billing, and prompt library operations. It is explicit about the supported capability domains (find an MCP, run a tool, get a prompt). It distinguishes itself from siblings by explaining the high-level coverage of the whole marketplace, which is a distinct purpose compared to simpler atomic 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 gives explicit when-to-use guidance: it names the core flow (action=search → describe → invoke), contrasts invoke with install, notes billing/auth link expectations and retry behavior, specifies when install is appropriate (permanent toolkit additions) versus invoke (one-off/occasional). It also distinguishes backup actions like list_tools, subscribe/cancel, report_bug, and request_mcp.

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 the safety profile with readOnlyHint=false, destructiveHint=false, and idempotentHint=true. The description adds useful reproduction-related context, but it does not disclose delivery mechanics, privacy implications of sending the conversation, or what feedback the user should expect.

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 sentences with no filler. It front-loads the tool's purpose and immediately gives the single important usage requirement, so every sentence earns its place.

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 feedback tool, the description is minimally adequate: it states the action, hints at the required message, and tells the agent to include recent messages for reproduction. However, it omits explanation of the context field, the string-serialized conversation mismatch, and what happens after submission.

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 explain the parameters, but it only meaningfully addresses conversation. Message and context remain under-specified, and the description calls conversation an array while the schema types it as a string, which could confuse the agent.

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-resource pair ('Report a bug, missing feature, or send feedback'), clearly defining the tool's scope. It is unambiguous and distinct from unrelated sibling tools like show_version or toolkit_info.

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 clearly implies the tool should be used when a user reports a bug, requests a feature, or sends feedback, and it adds a practical instruction to include the conversation for reproduction. It does not explicitly name alternatives or exclusions, but no sibling tool competes for this purpose.

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 aligns with the annotations (readOnly, idempotent, non-destructive). It doesn't add extra behavioral details, but since annotations already provide strong safety hints, the description is consistent and 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 a single, concise sentence with no redundancy or filler. It communicates the purpose efficiently.

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 simplicity of the tool (no parameters, no output schema), the description provides sufficient context. It fully explains what the tool does without needing additional details.

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, so schema coverage is 100% (nothing to document). The description doesn't need to explain parameters, and the baseline score of 3 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: to show current MCP platform and adapter versions. It uses a specific verb ('show') and resource ('current MCP platform and adapter versions'), distinguishing it from sibling tools like authenticate or connect.

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) and is straightforward. It doesn't explicitly mention alternatives or non-use cases, but given the simple nature and lack of parameters, the usage 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; the description adds important context beyond that: no platform credentials needed, prepaid per-query cost, data is non-confidential, and the client is the LGPD data controller. This is strong, though it does not mention rate limits, pagination, or response shape.

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 compact and front-loaded: it states purpose first, then credentials, cost, and legal context. Each clause adds useful information; only the long comma-separated sentence structure keeps it from being perfectly clear and scannable.

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 covers many contextual layers—official source, payment, authentication, data sensitivity, and LGPD—but remains incomplete for actual invocation: it does not state whether one of the three identifiers is mandatory, how to choose between them, or what the response contains. With no output schema and no parameter descriptions, this gap matters.

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%, and the description does not explain the ie, cpf, or cnpj parameters, their expected formats, or which one should be provided—harder because the schema declares zero required parameters. The field names are recognizable Brazilian identifiers, which prevents a score of 1, but the invocation semantics are still largely unsupported.

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 as a SINTEGRA Tocantins consultation against official Brazilian sources, with phrases like 'SINTEGRA: TO, consulta em fonte oficial' and 'consulta informações de fontes e órgãos oficiais brasileiros.' It stops short of a 5 because it never explicitly names the subject entities (IE, CPF, CNPJ) in the description and does not explicitly differentiate from other data-query siblings, though none is present.

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 gives usable operational guidance: use this tool for official, non-confidential citizen-available data, without platform credentials, paying per query with prepaid credits. It does not provide explicit when-not-to-use statements or alternatives, so it misses the top score.

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 behavior is covered. The description adds meaningful behavioral detail by naming the specific fields returned (connection status, accounts, catalog tool counts), going beyond 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 a single, well-structured sentence that front-loads the primary action and result ('Returns the current toolkit state') and then enumerates the included details. There is no wasted text or redundancy.

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, zero-parameter introspection tool, the description fully specifies the return contents and does not require further qualification. Sibling tools and annotations provide enough surrounding context, and no output schema exists, but the enumeration makes the tool's output expectations clear.

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 of 4 applies; there is no parameter ambiguity. The description fully covers the tool's input surface by having no inputs at all.

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 a specific verb ('Returns') and resource ('current toolkit state'), then enumerates the exact contents: installed MCPs, connection status, connected accounts, and catalog tool counts. This scope distinguishes it from sibling tools like show_version, which would only report 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 makes the tool's purpose evident—checking the current toolkit state and inventory—which implies use for introspection and status checks. It does not explicitly name alternatives or exclusions, but the sibling set makes the differentiation reasonably clear.

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