MDA CAF: Unidade Familiar de Produção Agrária (UFPA)

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

Annotations indicate idempotentHint=true and non-read-only, which the description aligns with. It adds valuable behavioral details: calling with no args returns a link for login, pasting a token enables session-only access, and adding to config yields a permanent token. This goes beyond the structured annotations, offering insights into the authentication workflow without contradicting any 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 single, information-dense sentence that efficiently communicates the key steps and modes without redundancy. Every phrase adds value, though it could be split for readability. It is appropriately concise 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?

For a simple tool with one optional parameter and no output schema, the description covers all necessary aspects: the purpose, the authentication flow, the two methods, and the optionality of the argument. It is entirely sufficient for an agent to 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?

With 0% schema description coverage, the description fully compensates by explaining the 'token' parameter: it is optional, expects a JWT string, and is used after user pastes it. It also clarifies behavior when omitted (no args returns the login link). This provides complete semantic meaning beyond the bare 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: to authenticate an MCP.AI user for IDE agents. It explicitly describes the login process, token retrieval, and two usage modes (permanent via config or session-only via pasted token). It effectively distinguishes itself from siblings by focusing on the authentication flow, making it unambiguous.

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: for IDE agents needing authentication. It gives step-by-step instructions and explains the two methods (config-based and session-based). However, it does not explicitly mention alternatives or exclusions (e.g., 'use connect for WebSocket connections'), but the guidance is strong enough to infer appropriate usage.

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 useful behavioral context about the response states (authenticated:true vs connect_url), which goes beyond annotations. However, it doesn't mention any potential side effects or rate limits, but given the annotations, a 3 is appropriate.

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, no filler, front-loaded with the core purpose. Every clause adds information about the response states. Excellent conciseness.

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 good annotations, the description is complete. It explains the two main response states (all connected vs missing credentials) and mentions per-install URLs. No output schema exists, but the description covers the key return values. Slight gap: doesn't mention what happens in partial connection states, but that's minor.

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 doesn't need to explain parameter semantics. The schema coverage is 100% (vacuously), and the description focuses on output behavior, which is the relevant semantic content. Baseline 4 for zero-param tools 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 returns connection status and URLs, and distinguishes it from siblings like authenticate and toolkit_info by focusing on status rather than initiating auth or general info. It's specific about the resource (connection status) and the output (authenticated flag, pending array, connect URLs).

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 it (to check connection status and get URLs when credentials are missing) but doesn't explicitly contrast with siblings like authenticate or toolkit_info. It provides context on what the response looks like in different states, which helps the agent decide, but no explicit 'use this instead of X' 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?

The description richly discloses behaviors beyond annotations: invoke works without installation (one-off execution), returns connect link for auth, returns checkout/top-up link for payment gaps, install makes permanent, search/describe flag installation status, and writes require specific roles. This context is far beyond the generic annotations (readOnlyHint=false, openWorldHint=true). 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 long but dense with value, structured as a single coherent paragraph covering core flow, key behaviors, use cases, and prompt library sub-system. It is front-loaded with the core flow. Slightly could be broken into shorter sentences or bullet points, but the information density justifies the length.

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 complexity (23 params, 14 actions, no output schema), the description gives a comprehensive overview of all major action categories and flows; however, it does not cover every parameter's semantics (e.g., resume vs subscribe/cancel, immediate flag, conversation, prompt_tool, prompt_targets), nor does it explain return values. Since there is no output schema, some additional detail on return formats would help. Still, it is quite complete for a marketplace aggregator 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 description names the key action parameter and its many enum values, plus explains the role of search, describe, invoke, install, list_tools, subscribe/cancel, report_bug, request_mcp, and prompt actions. However, it does not explain each of the 23 parameters in detail (e.g., limit, query, mcp_id, tool_id, arguments, immediate, tier_slug, prompt_*). Since schema coverage is 0% and many params are generic strings with defaults, the description compensates for the most critical params (action, mcp_id, tool_id, arguments) but not 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 this is the official mcp.ai marketplace catalog and execution engine, with a broad but well-defined purpose covering search, describe, invoke, install, and prompt library actions. It distinguishes from siblings by being the in-platform catalog and runner, while siblings like authenticate, connect, report_bug, show_version, toolkit_info are separate utilities.

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?

Provides explicit guidance: use search to discover, describe to get details, invoke for one-off runs even if not installed, install only for permanent inclusion, list_tools for current callables, subscribe/cancel for billing, report_bug for feedback, request_mcp for new MCPs. Also explains when to prefer invoke over install and notes authorization requirements (workspace owner/admin for writes). Differentiates from siblings by naming them as independent tools.

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 establish readOnly, idempotent, and non-destructive behavior. The description adds critical behavioral context: payment per query with prepaid credit, no platform credentials, data is public/non-confidential, and the client is data controller under LGPD. This goes well beyond what annotations 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 concise with four sentences, front-loading the main purpose. It packs relevant operational and legal details without unnecessary repetition, though the structure could be slightly improved by separating payment/legal specifics into a clearer sequence.

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 (one parameter, read-only), and the description explains the data source, payment, and legal responsibility. However, it lacks any indication of the expected return data or examples of input, which is a notable gap given no output schema exists.

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% and the description does not mention the 'cpf' parameter at all. The agent receives no explanation of what CPF is, its format, or how to use it, leaving the input completely underspecified.

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 explicitly states it is a query ('consulta') for 'Unidade Familiar de Produção Agrária (UFPA)' from the official MDA CAF source. It clearly distinguishes itself from sibling platform utilities (authenticate, connect, etc.) by naming the specific resource and operation.

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: it is hosted by the platform, requires prepaid credit, and does not need platform credentials. It also clarifies the legal context (LGPD). While it doesn't explicitly state when not to use it, the sibling tools are unrelated, so the guidance is sufficient.

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=false, destructiveHint=false, and idempotentHint=true. The description does not contradict these. It adds context that the tool sends feedback and includes conversation for reproduction, which is beyond the annotations. However, it doesn't disclose what happens after reporting (e.g., whether it creates a ticket, sends an email) or any side effects, but given the annotations, the bar is lower and the description adds some value.

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, two sentences, and front-loaded with the purpose. It includes a specific instruction about the conversation array. No wasted words, but it could be slightly more structured by explicitly naming parameters, but overall it's 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?

Given the tool's simplicity (3 params, no output schema, no nested objects), the description is mostly adequate. It covers the main purpose and hints at the conversation parameter. However, it lacks details on the 'context' parameter and doesn't explain what happens after submission. For a feedback tool, this is acceptable but could be more 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?

Schema description coverage is 0%, so the description must compensate. It mentions 'conversation array' and 'recent messages for reproduction', which adds meaning to the 'conversation' parameter. However, it does not explain 'message' or 'context' beyond what the schema provides (which is just type and default). The description partially compensates but leaves gaps for two of three parameters.

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 uses a specific verb ('report') and resource ('bug/feature/feedback'), and distinguishes it from sibling tools like 'show_version' or 'toolkit_info' by focusing on user feedback. However, it doesn't explicitly differentiate from potential similar tools, but given the sibling list, it's clear enough.

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 it (when reporting bugs, missing features, or feedback) but does not explicitly state when not to use it or mention alternatives. It provides a hint about including the conversation array for reproduction, which is useful context, but lacks explicit exclusions or comparisons to other tools.

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 does not need to restate safety. It adds the specific resource scope (platform and adapter versions) but does not describe return format or other behavioral details.

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?

Single sentence, no filler, and the key information is front-loaded. It is appropriately sized for a zero-parameter informational tool.

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, zero-parameter, read-only version tool with strong annotations, the description is complete enough. It states exactly what will be shown and requires no further elaboration on parameters or side effects.

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 schema coverage is 100%, so there are no parameter semantics to clarify. The description correctly focuses entirely on what the operation reports, which satisfies the zero-parameter baseline.

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 ('Show') and identifies the exact resource ('current MCP platform and adapter versions'). This clearly distinguishes it from sibling tools like authenticate, connect, and report_bug.

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 implicitly signals use when version information is needed, but it does not explicitly state when to use it versus alternatives, nor does it mention exclusions or prerequisites. It is clear but lacks explicit usage 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety traits. The description adds the details of what state is returned, which is useful but does not disclose potential latency or external dependencies beyond that.

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 purpose and enumerates the key output components. No wasted 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?

Given the tool has no parameters and the output is a state summary, the description covers the essential return categories. The lack of an output schema means the description bears more weight, but it is sufficiently detailed for a simple informational tool. It could optionally mention whether results are cached or real-time, but that is minor.

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 schema provides no semantics. The description explicitly lists the categories of information returned (installed MCPs, connection status, accounts, catalog counts), which compensates for the lack of parameters and adds clarity about output scope.

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 returns toolkit state including installed MCPs, connection status, connected accounts, and catalog tool counts. This is specific and distinguishes 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 usage for checking toolkit state but does not explicitly state when to use it versus alternatives. Sibling tools have distinct purposes, so context is clear, but no exclusions are provided.

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