Prefeitura SP São Paulo: CCM

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

Annotations indicate idempotentHint=true and destructiveHint=false, which the description aligns with by describing both session and permanent setup without implying harmful side effects. It adds useful context about token format and the two login flows, going beyond the annotations. It doesn't explicitly state what happens on success, but the behavior is clear enough.

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, front-loaded with the core purpose, and uses a clear structure (purpose, best practice, session alternative, usage instructions). Every sentence adds necessary information without 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 tool's simplicity (one parameter, no output schema, minimal annotations), the description covers all needed aspects: authentication workflow, token usage, and both permanent and session modes. It is complete for an agent to correctly invoke this 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?

Schema coverage is 0%, but the description fully compensates by explaining what 'token' means (a JWT) and how to use it: either passed as an argument or omitted to get a link. This is exactly the meaning the schema lacks, so the description adds high value.

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 via browser login and token acquisition. It specifies the resource (auth token) and the action (login), and distinguishes it from siblings like 'connect' by its focus on authentication. However, it doesn't explicitly contrast with alternative tools, which costs a point.

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 usage context: it explains when to use the tool (for IDE agents needing auth), and gives two modes (permanent config vs session-only token). It also tells how to call it with or without a token. It doesn't mention when not to use it or alternatives, but the context is specific enough.

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 clear. The description adds significant value by detailing exactly when authenticated:true appears, what pending[] contains, and what happens when credentials are missing, including the connect_url fields. No contradictions 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?

Two sentences, front-loaded with the main verb 'Returns,' and each sentence provides essential information without redundancy. The structure is clear and directly addresses the tool's purpose and output conditions.

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 status endpoint with no output schema, the description fully covers the conditional responses and URL details. There are no gaps in what an agent would need to know to invoke the tool correctly and interpret its results.

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 coverage is trivially 100%. The baseline for no parameters is 4, and the description doesn't need to add parameter details. It appropriately focuses on output behavior instead.

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' with specific conditional behaviors, making it immediately obvious what the tool does. It distinguishes itself from siblings like 'authenticate' and 'marketplace' by focusing on status/URL retrieval.

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 when needing connection status or URLs but provides no explicit guidance on when to choose this over alternatives, nor does it name any sibling tools. The conditional behaviors give context but no direct 'use this when' 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 provide only generic flags (readOnlyHint=false, openWorldHint=true), so the description carries the transparency burden, and it delivers richly: detailed one-off execution behavior ('runs it pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list'), auth flows (invoke returns a connect link when credentials needed, checkout/top-up link when wallet empty), write-permission requirements, and prompt-library share behavior (opens without login). No contradiction with annotations — in fact it deepens what readOnlyHint=false implies by specifying exactly which operations mutate.

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 wall of text covering 14 actions across two domains. It front-loads the core concept and uses 'Core flow:' and 'KEY:' markers to highlight critical info, which helps navigation. However, given the bulk and the two distinct sub-domains (MCP operations vs prompt library), it would benefit from more structured separation. The length is somewhat justified by the complexity, but readability suffers. Every sentence earns its place, yet structure is only average.

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?

Despite the high complexity (14 actions, 23 params, no output schema, generic annotations), the description is remarkably thorough: full end-to-end flow, edge cases (credentials, empty wallet, retry), permission model, and the second prompt-library domain fully explained. Weaknesses: it never specifies per-action return formats (which matters with no output schema) and action-specific nuances for params like immediate, tier_slug, and conversation are absent. Strong but with identifiable gaps.

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% for all 23 parameters, so the description must compensate. It does explain the semantic meaning of several key params through workflow context (action driving the whole flow, tool_id/arguments for invoke, mcp_id for describe, query for search). However, many parameters remain unexplained in the description (prompt_title, cancel_comment, conversation, immediate, tier_slug, request_name, report_context, etc.), leaving real gaps for the agent. Partial compensation, not full.

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 is the official mcp.ai marketplace catalog plus the runner for MCPs, with a specific scope. It explains the core action flow (search→describe→invoke) and distinguishes itself from siblings like authenticate and show_version by covering catalog/execution/billing/prompt-library functions. It's broad because it covers 14 actions across two domains (MCP marketplace + prompt library), which slightly dilutes the single-verb clarity, but the purpose is 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?

Extremely explicit guidance on when to use each action: 'prefer invoke for a single/occasional use' vs 'Use install only to make an MCP PERMANENT', 'Search/describe flag installed_in_toolkit vs installed_in_workspace', and clear alternatives like 'request_mcp asks us to build a NEW MCP when nothing fits'. The sequential core flow (search→describe→invoke) provides step-by-step triggering context, and permission requirements are stated (writes require workspace owner/admin). This is exemplary 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?

Beyond the annotations (readOnly, idempotent, not destructive), the description adds crucial behavioral context: it's hosted by the platform, payment is per query without platform credentials, data is from official sources, and LGPD compliance details. This enriches understanding of the tool's operation and constraints.

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 front-loads the purpose and is moderately concise Mickeleit; the single sentence packs a lot of information about the tool's scope, credentials, payment, and legal context. However, it's a bit of a run-on with semicolons, but overall 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?

The description covers legal/policy context well but fails to explain input parameters, return format, or any examples. With no output schema and zero parameter coverage, the agent is left without essential operational details like what data is returned or how to structure the query.

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 two parameters (cpf, cnpj) with zero description coverage in the schema itself, and the description does not mention them at all. It fails to explain what these parameters mean or how to use them, despite there being no schema descriptions to rely on.

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 it is a consultation for the São Paulo city CCM (Municipal Taxpayer Registry) from official Brazilian agencies. The tool name itself also indicates the purpose. However, it doesn't explicitly define what CCM is or the specific data 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: hosted by the platform, no platform credentials needed, pay-per-use, official data, and LGPD compliance. However, it does not specify whether the client must supply both CPF and CNPJ or either one, nor does it mention any prerequisites or alternatives among sibling 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 disclose readOnlyHint=false, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds a small instruction about including conversation for reproduction but doesn't describe side effects (e.g., whether a ticket is created, where feedback goes). This is limited extra value, so 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?

The description is exactly two sentences, front-loaded with the primary purpose, and includes only necessary guidance about the conversation array. Every word earns its place with no redundancy or 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?

For a simple tool with three parameters and no output schema, the description is adequate. It covers the core action and the most important parameter (conversation). The only gap is the unexplained context parameter, but overall it provides sufficient information for an agent to use 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?

Schema description coverage is 0%, so the description must compensate. It explicitly explains the conversation parameter (include recent messages for reproduction) and the message parameter is implied as the main text. The context parameter remains unexplained, but the most critical parameters are meaningfully described, providing partial 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 defines the tool's purpose with specific verbs ('Report a bug, missing feature, or send feedback') and a clear resource (feedback submission). It distinguishes itself from sibling tools like authenticate and connect, which serve entirely different functions. The scope is broad but 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 implies solid usage context: use when you need to report a bug or provide feedback. It gives explicit guidance to include the conversation array for reproduction, helping correct usage. It doesn't explicitly mention alternatives or when-not-to-use, but no sibling offers a similar capability, so exclusion isn't necessary.

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 does not disclose behavioral traits beyond what the annotations already provide (read-only, non-destructive). It adds minimal extra information, such as 'current', which suggests a snapshot but does not reveal side effects or edge cases.

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 extremely concise, using a single sentence to convey the essential information. It avoids any fluff or unnecessary details, fitting the requirement for brevity.

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?

Since there is no output schema, the description serves as the sole explanation of what is returned. It states that versions are shown, which is sufficient for a simple tool, but it does not elaborate on the exact format or structure of the output, leaving some minor ambiguity.

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 no parameters, so the description correctly avoids any parameter details. The tool's functionality is fully explained without needing parameter documentation, making this aspect effectively complete.

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: showing current MCP platform and adapter versions. It is unambiguous and distinct from sibling tools like authenticate or connect, leaving no doubt about its purpose.

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 does not explicitly specify when to use this tool versus alternatives, but given its simplicity and self-explanatory nature, it is likely obvious. It provides no conditional guidance or context clues, which could merit a higher score if 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=true, idempotentHint=true, and destructiveHint=false, so safety is established. The description goes further by detailing what data is returned (installed MCPs, connection status, accounts, catalog tool counts), adding behavioral context beyond the annotations without contradicting them.

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, front-loaded sentence: it opens with 'Returns the current toolkit state' and then lists the four specific data elements it covers. Every clause adds information without filler, making it both concise and clearly structured.

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?

With no output schema, the description must explain return values, and it does so thoroughly: installed MCPs, their connection status, accounts connected, and catalog tool counts. For a zero-parameter, read-only informational tool with strong annotations, this is complete enough for an agent to decide when and how to use it.

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 documentation burden. The baseline for zero-parameter tools is 4, and the description adds no unnecessary param detail, which is appropriate since there are no inputs to explain.

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 a clear resource ('current toolkit state'), then enumerates exactly what is included: installed MCPs, their connection status, connected accounts, and catalog tool counts. This unambiguously distinguishes it from sibling tools like authenticate or connect, which perform actions rather than report state.

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 to inspect the state of the toolkit, such as installed MCPs, account connections, or catalog tool counts. It does not explicitly mention alternatives or when-not-to-use cases, but for a zero-parameter informational tool with siblings focused on actions (authenticate, connect, marketplace), the intended usage is obvious.

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