MP Mato Grosso (Procedimentos)

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

The description clarifies the tool's behavior: no args returns a link, token input enables session login. It adds context about permanent vs session connections beyond the annotations, which already indicate idempotent and non-destructive.

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-loads the purpose, but it's a bit dense with multiple clauses. Still, every sentence provides necessary 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 simple tool with one optional param and no output schema, the description covers the main scenarios: getting a link, pasting a token, and recommending permanent config. It could be clearer about the agent's workflow but is generally 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?

The schema only defines a 'token' string with no description. The description explains the token's role, that it's optional, and the alternative of calling with no args. This significantly compensates 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 the tool's purpose: to authenticate IDE agents to the MCP.AI server via browser login and access token. It distinguishes from sibling tools by focusing on authentication and provides specific usage modes.

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 explains the two ways to authenticate (permanent header config vs session token) and the exact invocation patterns (with/without token). However, it does not explicitly mention when not to use this tool compared to 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 mark the tool as read-only and idempotent, and the description adds meaningful state-dependent behavior: when all providers are connected it returns authenticated:true and empty pending[], and when credentials are missing it returns connect_url for the toolkit and per-install URLs. This goes beyond the annotations, though partial connection states are left ambiguous.

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 only two sentences: the first states the primary purpose and the second gives conditional outcomes. It is front-loaded, concise, and contains no filler words.

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, simple status-checking tool with no output schema, the description adequately explains the main return values and the two key states (fully connected and missing credentials). Combined with annotations, it provides enough context for an agent to select and invoke 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 tool has 0 parameters and full schema coverage, so there is no parameter meaning to add. The baseline of 4 applies because the schema already makes clear that no input is required, and the description does not need to add further parameter detail.

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' with a specific verb and resource. The conditional results distinguish it from sibling tools like authenticate (action-oriented) and show_version/toolkit_info (different 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 implies usage by describing when to call it to check connection status and shows outcomes under different conditions, but it never explicitly states when not to use it or names alternatives like authenticate. Usage guidance is inferred rather than stated.

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 state non-read-only, non-idempotent, non-destructive, and open-world. The description adds substantial behavior: invoke works for non-installed MCPs, returns connect links for auth and top-up links for empty wallets, mentions owner/admin permission for writes, and notes that install adds tools permanently while invoke runs one-off without bloating the list. 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 long but every sentence adds distinct value for a 14-action tool. It is front-loaded with the core purpose and progresses logically from core flow to key invoke behavior, install vs invoke, then auxiliary actions and the prompt library. Could be improved with structural separation, but remains efficient for its 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?

For a tool with 23 parameters, 14 actions, no output schema, and minimal annotations, the description covers the central search→describe→invoke flow, permission requirements, and one-off vs permanent semantics. Gaps remain: the 'resume' action is not explained, the 'immediate' flag and pagination limits are not documented, and exact return shapes are not described, leaving some uncertainty for edge-case actions.

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 compensates by explaining the main action parameter and the roles of mcp_id, tool_id, arguments, and prompt-related fields in the flow. However, many parameters like limit, immediate, tier_slug, cancel_reason, report_context, request_details, prompt_targets, conversation, and prompt_tool are not semantically explained, requiring agent inference.

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 opening sentence clearly identifies the tool as 'the official mcp.ai marketplace' and the way to run MCPs, with a specific verb+resource. It enumerates the core actions (search, describe, invoke) and distinguishes this from sibling tools like authenticate, connect, and show_version by focusing on catalog/search/run capabilities.

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: 'Use install only to make an MCP PERMANENT', 'prefer invoke for a single/occasional use', and 'list_tools lists what is callable right now'. It also clarifies when to use subscribe/cancel, report_bug, request_mcp, and the prompt library actions, forming clear decision rules between 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?

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds valuable behavioral context: no credentials required, payment model, the public nature of the data, and LGPD compliance responsibilities. This helps the agent understand the operational and legal constraints without contradicting 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 concise and front-loaded, with the main purpose stated first. Each sentence adds meaningful information (access, payment, data nature, legal responsibility) without redundancy. It is appropriately sized for the tool's 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 covers the data source, public access, payment, and legal considerations, providing solid context for an agent to decide when to use it. However, given no output schema, it does not describe the return format or content, leaving some uncertainty about what the tool actually 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?

The schema has 0% description coverage, so the description must compensate. It explains that Cpf/Cnpj are used to query for a person or company, but does not clarify the 'completo' boolean parameter or the fact that the schema requires both Cpf and Cnpj despite the text suggesting 'ou' (or). Partial compensation, missing key 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 clearly states a specific action ('Consulta') on a specific resource ('procedimentos investigatórios extrajudiciais do Ministério Público de Mato Grosso') for a person or company using CPF or CNPJ. It effectively distinguishes itself from the unrelated sibling tools like authenticate or 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?

The description provides clear context on when to use the tool: hosted on the platform, no credentials needed, pay-per-query with prepaid credit, and it queries public-access information. It does not explicitly mention alternatives or when-not-to-use scenarios, but the context is sufficient given no similar sibling tools exist.

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 indicate non-read-only, non-destructive, and idempotent behavior. The description adds the useful instruction to include the conversation array for reproduction, which is context beyond annotations. It does not contradict 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 concise sentence that front-loads the purpose and includes key invocation guidance. There is no redundant 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 tool with no output schema, the description covers the main purpose and the most important parameter ('conversation'). It does not explain the 'context' parameter or what the expected response is, but the tool is straightforward enough that this is not a critical gap.

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 compensate. It explains the 'conversation' parameter's role but leaves 'message' and 'context' implicit. This partial explanation raises the score above baseline, but more parameter detail would be needed for full compensation.

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 sends feedback. This distinct action and resource set it apart from sibling tools like authenticate, 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 when to use the tool (when a bug, missing feature, or feedback needs to be submitted) and provides guidance to include the conversation array for reproduction. It does not explicitly mention when not to use it or alternatives, 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 false, covering safety. The description adds specificity about platform and adapter versions but no additional behavioral context like output format or side effects. It does not contradict 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?

A single, concise sentence with no filler. The verb and resource are front-loaded, making the purpose immediately clear.

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 version check tool, the description is fully sufficient. No output schema is needed to understand the operation, and the simplicity of the tool means no further elaboration is required.

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 description coverage is trivially 100%. The description correctly implies no inputs are needed. Baseline for 0 parameters is 4.

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 specifies the resource (current MCP platform and adapter versions). It is distinct from sibling tools like authenticate or marketplace, being the only tool for version inspection.

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 for checking versions but gives no explicit guidance on when to use it, when not to, or which alternatives might exist. The context is straightforward, but the 'when-to-use' 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 cover read-only and idempotent behavior. The description adds useful context about what the return value includes (installed MCPs, connection status, etc.), which helps understand the tool's output. No contradictions were found.

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 with no fluff, front-loading the main purpose and listing specifics concisely. Every word 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?

Given no output schema, the description adequately covers what the tool returns by listing the key state components. It's complete for a simple read-only no-parameter tool, with rich annotations already providing safety 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 description correctly avoids parameter details. The baseline for 0 params is 4; the description doesn't need to add 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 clearly states the tool's function with a specific verb ('Returns') and resource ('current toolkit state'), enumerating the exact contents: installed MCPs, connection status, accounts, and catalog tool counts. This distinguishes it from sibling action 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 the tool is for checking toolkit state but does not explicitly state when to use it or mention alternatives. It lacks guidance on prerequisites or exclusions, so usage context is only implied by the tool's nature.

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