SINTEGRA: SC

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

Annotations already provide safety hints (idempotent, non-destructive), and the description adds valuable context: the browser login step, the distinction between permanent config-based auth vs session-only token paste, and the behavior of calling with no arguments. It doesn't mention error handling or token invalidation, but the added context is meaningful.

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?

Three short sentences/sentence fragments cover context, best-practice config, and runtime usage. No redundant filler; each clause contributes actionable information. The structure effectively front-loads the purpose.

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 1-optional-param tool with no output schema, the description covers the main behaviors: obtaining a link and logging in with a token. It doesn't explicitly state the return value when a token is provided (e.g., success message), but the outcome 'session-only login' is implied. Minor gap given 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?

Schema coverage is 0%, so the description carries the full burden. It clearly explains the 'token' parameter as a JWT to paste for session login, and notes that omitting it returns the login link. The optionality and purpose of the parameter are fully clarified.

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 is the authentication/login tool for MCP.AI IDE agents. It specifies the action ('log in in the browser, copy the access token') and the two invocation modes, distinguishing it from sibling tools like connect or toolkit_info 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?

It gives explicit guidance on when to call with a token (session-only login) versus with no args (get the link), and recommends a persistent config header as an alternative to the runtime call. It lacks explicit 'don't use this when...' wording, but the two-path guidance 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 and destructiveHint: false, aligning with the non-mutating description. The description adds value by detailing the return conditions (authenticated:true vs connect_url), which give the agent a clear picture of behavior without needing to call the 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?

Two sentences, front-loaded with the primary purpose, then conditional behavior. Zero wasted words; every sentence adds value.

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 tool is simple with no parameters and no output schema. The description explains the response conditions ('authenticated:true', 'empty pending[]', 'connect_url') and distinguishes between toolkit-level and per-install URLs. This is sufficient context for a status-checking tool, though it doesn't enumerate all possible failure modes.

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 baseline is 4. The description focuses on return values, which is appropriate since there's nothing to document about parameter usage.

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: 'Returns connection status and URLs.' It goes further to describe the two possible states (all providers connected vs missing credentials), giving a precise scope of what the agent can expect.

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 explains when the tool returns different results (all providers connected vs. missing credentials), implying when it's useful for checking connection status. It does not explicitly name alternatives or prerequisites, but the conditional behavior serves as 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?

Goes far beyond annotations by disclosing authorization requirements ('Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin'), and describes retry semantics for auth ('invoke returns a connect link') and payment gaps ('invoke returns a checkout/top-up link (the user opens it, then you retry)'). 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?

Information-dense and every sentence earns its place, but it's a single wall of text mixing different concerns (search flow, invoke semantics, billing, permissions, prompt library). No bullet points or section breaks hurt scannability for such a long spec.

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?

Comprehensive coverage of the entire surface: discovery, profiling, execution, install-vs-invoke semantics, permission model, billing/auth edge cases, prompt library, and report/request paths. The only minor omission is lack of example values, but the description comfortably fills the gap left by absent output schema and sparse schema docs.

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?

Despite 0% schema coverage, the description conveys workflow semantics for key parameters (action=search → describe → invoke, mcp_id, tool_id, arguments implied). Modal verbs like 'prefer invoke' add intent. Doesn't enumerate every one of the 23 params, but the flow is so well-documented that gaps are non-critical.

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?

Instantly clear this is the master tool for the mcp.ai marketplace: 'the in-platform catalog of every MCP/tool, AND the way to run them'. It names specific capabilities (search, describe, install, invoke) and maps them to a flow, distinguishing it from sibling tools like authenticate/connect which handle auth.

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?

Excellent when-vs-alternatives guidance: 'Use install only to make an MCP PERMANENT in the active toolkit... prefer invoke for a single/occasional use' and 'list_tools lists what is callable right now'. Also contrasts the prompt library sub-domain ('rather than MCPs').

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: true and destructiveHint: false, so the description need not repeat safety. It adds context about including conversation for reproduction, which is useful. It doesn't discuss side effects, but annotations cover most.

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 sentences, every word earns its place. It is concise and front-loaded with the primary purpose.

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 tool is simple, but without output schema or schema coverage, the description must fully explain behavior and parameters. It fails to describe the 'context' parameter and does not indicate what happens after reporting. Given the lack of structured information, it is incomplete.

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 parameters. It mentions 'message' and 'conversation' but not 'context'. It says to include conversation, but does not explain the format or purpose of 'context'. This is insufficient for a 3-parameter tool with zero schema documentation.

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 reports bugs, missing features, or feedback. It is specific enough to distinguish from siblings like marketplace or show_version, though it could benefit from naming specific alternatives.

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 says to include the conversation array for reproduction, implying when to use it (for bug reports) but does not explicitly state when not to use it or contrast with other tools. Guidance is minimal but present.

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. The description adds specific context about the tool's output: it shows both MCP platform and adapter versions, which is more detailed than the annotations. No contradiction; it aligns with the read-only, idempotent nature.

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 single, succinct sentence that is front-loaded with the action and scope. Every word contributes meaning, and there is no 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 tool with no parameters, no output schema, and simple read-only behavior, the description provides sufficient context. It names the exact items reported (platform and adapter versions). Some might expect a note about return format, but given the simplicity, this 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?

There are zero parameters in the schema. Baseline for no parameters is 4 per rubric. The description does not need to explain parameters, as 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 clearly states the action (show) and the specific resource (current MCP platform and adapter versions). It is specific enough to distinguish from generic version tools and matches the tool name 'show_version'. No ambiguity.

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 explains what the tool does but does not provide explicit guidance on when to use it versus alternatives, such as 'toolkit_info' or other informational tools. For a simple version check, the purpose is self-evident, but no alternatives or exclusions are mentioned.

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 readOnlyHint: true, idempotentHint: true, and destructiveHint: false, and the description aligns by mentioning it's a query that returns data available to citizens without confidential info. It adds useful context about data controller responsibilities (LGPD), though it doesn't specify return format, rate limits, or exact data fields returned.

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 composed of two concise sentences that pack meaningful context: official source, no credentials, prepaid credits, citizen-level data, and LGPD compliance. While dense, it covers key points 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?

Given the complexity of regulatory and payment context, the description covers legal and operational aspects well, but lacks what the actual response contains, example usage, or preconditions (e.g., sufficient credit). For a tool with no output schema, more detail about return data would be helpful.

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 mentions the tool queries official sources and lists 'cpf', 'cnpj', and 'ie' (state ID) parameters in the schema, but the description doesn't explain which parameter to use when, or that exactly one is required. It doesn't compensate for the 0% schema coverage, leaving the agent guessing about semantics like state registration number.

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 the tool as consulting official Brazilian sources (SINTEGRA SC) and mentions it involves queries ('consulta'), but the purpose remains diffuse. It doesn't clearly state what a user achieves by calling it (e.g., get a taxpayer's registration status).

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 clear context about when to use the tool, including that it queries official sources without platform credentials, prepaid credits per query, and that it aligns with LGPD for legitimate purpose. This distinguishes it from sibling tools by emphasizing the official source and payment model.

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 description's transparency burden is lower. It adds useful context by specifying what 'state' means—installed MCPs, connection status, accounts, and tool counts—and notes that this is the current, live state. 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 entire description is one sentence, front-loaded with the core purpose ('Returns the current toolkit state') and then listing the relevant output categories. There is no fluff or repetition of schema/annotation 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?

Even though there is no output schema, the description enumerates all key components of the returned state, making the tool's behavior sufficiently clear for an agent to invoke it and interpret the result. Given zero parameters and strong annotations, the description is complete for this simple informational 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?

The tool has 0 parameters and the input schema is empty, so there are no parameter semantics to explain. Per the rubric, a no-parameter tool receives a baseline of 4; the description appropriately focuses on the output rather than inputs.

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'), then enumerates the exact contents: installed MCPs, connection status, connected accounts, and catalog tool counts. This scope clearly distinguishes it from siblings like show_version (version only) and connect/authenticate (mutating/action 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 provides clear context: use this tool when you need the current toolkit state, including connection and account status. It does not explicitly name alternatives or exclusions, but the read-only informational scope is unambiguous and differentiated from the action-oriented sibling tools.

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