MTE: Processos por Empregador

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

It discloses session vs. permanent authentication, non-expiring header option, and token-paste behavior. Annotations indicate idempotent and non-destructive, and the description adds context about how the token is used without contradicting those hints.

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 run-on sentence with informal phrasing ('Best:', 'paste it here'). It covers necessary points but could be organized into clearer steps.

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, it explains the input behavior well but doesn't specify what the tool returns after a token is provided or what errors may occur. The no-args link flow is described, but success/return messaging is left implied.

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 only a 'token' string with 0% description coverage, but the description fully explains the parameter's meaning (JWT access token) and includes the no-args alternative. This compensates for the sparse schema.

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 as logging into MCP.AI for IDE agents, with specific steps (browser login, copy token). It distinguishes itself from siblings by focusing on authentication for MCP.AI, though it doesn't explicitly contrast with the 'connect' sibling.

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 patterns: adding a header to config for permanent access, or passing a token for session-only login, or calling with no args to get a link. It doesn't explicitly mention when not to use it, 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds behavioral context beyond these by explaining the conditional returns (authenticated:true and empty pending[] vs. connect_url), which is valuable and goes beyond the structured metadata. No contradiction.

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 sentences, front-loaded with the core purpose and then elaborates on the two output states. Every sentence earns its place, no fluff or redundancy, and the structure is clear and efficient.

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 tool with no parameters and no output schema, the description is complete. It covers both main scenarios (all providers connected vs. missing credentials) and explains the resulting outputs. Given the low complexity, nothing else is necessary.

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 input schema is empty. Per the rubric, the baseline for 0 params is 4. The description does not need to add parameter semantics since there are none, and it correctly focuses on outputs.

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 uses a specific verb and resource, and further details the output conditions (authenticated:true when all providers connected, connect_url when missing). This distinguishes it from siblings like authenticate and 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 implies when to use the tool by explaining it returns connection status and URLs, and describes behavior in different states. However, it does not explicitly state when not to use it or mention alternatives, though the clear context of a status check makes its usage apparent.

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 annotations, it discloses that invoke runs one-off without installing, returns connect/checkout links for auth/payment, requires owner/admin for writes, and that prompt links open without login. This adds substantial behavioral context not present in 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 dense, single-paragraph wall of text covering many features. While most sentences add information, it lacks structure (bullets/separate sections) and includes a Portuguese word 'pontualmente' that may confuse. It is informative but not concise.

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 23 parameters, 14 actions, and no output schema, the description covers the main flows, auth/billing behavior, permission requirements, and the prompt library. However, it omits return-value formats and several parameter semantics (immediate, tier_slug, conversation), leaving some gaps for an agent to infer.

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, the description must compensate for 23 parameters. It explains the action enum and core parameters (mcp_id, tool_id, arguments, prompt_slug, prompt_vars), but leaves many parameters unexplained (immediate, tier_slug, conversation, message, request_name, request_details, report_context, cancel_comment). Partial compensation only.

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: it is the official mcp.ai marketplace catalog and execution engine. It enumerates specific actions (search, describe, invoke, install, list_tools, subscribe/cancel, report_bug, request_mcp, and prompt library functions), distinguishing it from sibling tools by covering the full MCP lifecycle.

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 explicitly prescribes a core flow (search → describe → invoke) and contrasts invoke vs install ('prefer invoke for a single/occasional use'). It also names when to use list_tools, subscribe/cancel, report_bug, request_mcp, and the prompt library, plus notes that writes require workspace owner/admin.

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 read-only and idempotent annotations, the description adds behavioral details like the payment requirement, the official source, non-confidential nature of data, and LGPD compliance. These are meaningful additions that help the agent understand the tool's behavior.

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 multiple sentences but no unnecessary filler. It is well-structured and covers several relevant aspects without being overly verbose.

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?

Although it provides context about the data source, payment, and legal compliance, it does not describe the expected output or the exact parameters. With no output schema and no parameter explanations, the description is incomplete for a user to fully understand how to use the 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 schema has no parameter descriptions (0% coverage), and the description does not explain the cpf, cnpj, or cnpj_raiz parameters. The overall employer context is implied, but the specific purpose and usage of each parameter remain unclear, so the description fails to compensate for the lack of 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 that the tool queries processes by employer from an official Brazilian source, which distinguishes it from the unrelated sibling tools. However, it could be more specific about the exact nature of the processes 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 usage context, such as the need for prepaid credits and access to official sources, but it does not explicitly state when to prefer this tool over alternatives. Since no direct alternatives exist among siblings, this is not a major issue, but it lacks explicit 'when to use' 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 convey that this is a non-read-only operation and not destructive, lowering the burden on the description. The description adds the reproduction context but does not disclose what happens after submission, whether duplicate reports are possible, or any response behavior.

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 focused sentence with no filler. It front-loads the purpose and adds only the essential reproduction guidance.

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 tool with one required string parameter, optional defaults, and no output schema, the description is largely sufficient. The context parameter is left unexplained, but this is minor given its optionality and the overall 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?

Schema descriptions cover 0% of parameters, so the description must compensate. It clarifies the purpose of conversation ('recent messages for reproduction') but leaves message and context semantics mostly implicit, with message only inferable as the report content.

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 ('Report') and the resource types ('bug, missing feature, or feedback'), making the tool's purpose immediately obvious. It also distinguishes itself from unrelated siblings such as authenticate and marketplace.

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 identifies the intended use cases and gives a concrete instruction to include the conversation array for reproduction. It does not name exclusions or alternatives, but no sibling tool competes with this feedback-reporting 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds no additional behavioral context such as auth requirements, rate limits, or side effects; it only names the resource, which is purpose-related rather than behavioral.

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?

One concise sentence, front-loaded with the verb and object. No wasted words or redundant structuring.

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 informational tool with good annotation coverage, the description fully captures its purpose and scope. No output schema exists, but none is needed given the straightforwardness.

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 zero parameters, so the description does not need to explain any. Baseline of 4 applies per the rubric.

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 distinct from all sibling tools, which cover authentication, connection, marketplace, etc. No ambiguity exists.

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 use case (retrieve version info) but does not explicitly contrast with alternatives or state when not to use it. No sibling tool directly overlaps, so the lack of exclusion is minor, but guidance remains implicit 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context by detailing what the returned state contains, which goes beyond the annotation metadata. It does not discuss freshness or permission requirements, but for a read-only info tool with strong annotations this is sufficient.

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, front-loaded sentence conveys the full behavior and output scope without repetition or filler. Every element in the description 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, parameterless read-only tool with rich annotations and no output schema, the description adequately explains what will be returned: installed MCPs, connection status, accounts, and catalog tool counts. No additional context is necessary.

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 there is no parameter burden for the description to carry. Baseline for zero-parameter tools is 4; the description does not need to explain parameters and instead focuses on output content.

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 exactly what that includes: installed MCPs, connection status, connected accounts, and catalog tool counts. This distinguishes it from sibling tools like show_version and 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 implies this is for inspecting toolkit state but does not explicitly state when to choose it over alternatives or when not to use it. For a no-parameter informational tool, the intended use is reasonably clear, but no direct comparison or exclusion is provided.

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