Conselho Federal de Farmácia: Cadastro, official-source lookup. Platform-hosted, pay per query with
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Average 4.1/5 across 7 of 7 tools scored. Lowest: 2.5/5.
Most tools have clearly distinct purposes: authenticate handles auth token, connect reports connection status, marketplace manages MCP catalog operations, and cff_cadastro_consultar is the only domain query tool. However, connect and toolkit_info overlap in reporting connection status, and marketplace's many internal actions (search, describe, invoke) could cause ambiguity with toolkit_info's list of installed MCPs.
Naming is inconsistent: some tools use plain verbs (authenticate, connect, report_bug), some use noun-style (marketplace, toolkit_info), and the domain tool cff_cadastro_consultar uses a different underscore-separated pattern. No coherent verb_noun or noun_verb convention is followed.
Seven tools is a moderate count, but for a server named 'Conselho Federal de Farmácia: Cadastro' only one tool is domain-specific. The remaining six are generic platform utilities, making the set feel under-scoped for the apparent purpose and heavily weighted toward infrastructure rather than the core domain.
The domain surface is severely incomplete: cff_cadastro_consultar provides only a single consultation operation, with no update, list, or administrative tools. While platform utilities (marketplace, authenticate, etc.) are comprehensive, they do not address the stated purpose of cadastro queries, leaving agents with a dead end for any non-consultation need.
authenticateAMCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header Authorization: Bearer <token> for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "" } after the user pastes, or with no args to get the link.
| Name | Required | Description | Default |
|---|---|---|---|
| token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits beyond annotations: the permanent vs. session-only nature of the two login methods, and that calling with no args returns a link. This complements the idempotentHint annotation without 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 somewhat verbose, spanning three sentences, but each sentence adds necessary context (the login flow, the preferred permanent method, the session-only alternative). It is front-loaded with the core purpose and avoids redundant filler.
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 explains the expected behavior: getting a link with no args and completing login with a token. It does not specify the exact return value for success/failure, but for a simple authentication tool 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?
The schema only defines an optional 'token' string with no description. The description explains that the token is a JWT pasted by the user, and clarifies the two call patterns (with token or no args). This fully compensates for the 0% schema coverage.
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: authenticate with MCP.AI for IDE agents. It details the login process and distinguishes between permanent config-based auth and session-only token-based auth, which differentiates it from sibling tools like '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 gives explicit instructions on two usage paths: adding a header to config for permanent access, or passing a token for session-only login. It also explains calling with no args to get a link. However, it does not explicitly state when to avoid this tool in favor of alternatives like 'connect'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cff_cadastro_consultarCConselho Federal de Farmácia: Cadastro, consulta em fonte oficial. Hospedado pela plataforma, sem credenciais da plataforma, pague por consulta com crédito pré-pago. Consulta informação de fontes e órgãos oficiais brasileiros (a mesma disponível ao cidadão), não é dado sigiloso. O cliente é o controlador dos dados e responde pela finalidade legítima (LGPD).
| Name | Required | Description | Default |
|---|---|---|---|
| uf | Yes | ||
| crf | No | ||
| nome | No | ||
| categoria | Yes | ||
| municipio | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent behavior. The description adds information about payment and data privacy (LGPD) but does not disclose other behavioral aspects such as rate limits, error handling, or response structure. This provides some additional context beyond 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 relatively short and avoids unnecessary detail, though it repeats the word 'consulta' multiple times. It is structured as a few sentences and is not 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?
The description does not explain what the query returns (no output schema is provided) or what kind of data is included in the response. It mentions official sources but lacks details on the expected result, making it incomplete for a clear understanding.
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 input schema includes parameters (uf, municipio, categoria, crf, nome) with no descriptions, and the tool description does not explain any of them. Schema coverage is 0%, and the description fails to compensate, leaving parameter meanings ambiguous.
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 indicates it is a query tool for official Brazilian sources, but does not specify what type of registration data is queried (e.g., pharmacy professionals, establishments). It clearly mentions 'consulta' but lacks specificity about the resource being accessed.
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 context that the tool is paid (pre-paid credit) and does not require platform credentials, but it does not explicitly state when to use this tool versus alternatives. No guidance on typical use cases or conditions is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
connectAReturns connection status and URLs. When all providers are connected, returns authenticated:true and empty pending[]. When credentials are missing, returns connect_url for the toolkit and per-install URLs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. The description adds value by explaining the returned structure (authenticated flag, pending array, connect_url) beyond what annotations provide, making the tool's behavior clearer.
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 at two sentences, front-loads the main purpose, and adds conditional details without fluff. Every sentence adds meaning.
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 zero-parameter simplicity and rich annotations, the description is complete. It explains both success and failure states. Minor gap: it doesn't specify what 'per-install URLs' mean or the exact structure of connect_url, but this is covered by the output schema absence and the description's clarity.
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 need not compensate. The description explains the output semantics clearly, which is the main value since there is no input schema to elaborate 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 the tool returns connection status and URLs, with specific conditions (authenticated:true, empty pending[], connect_url when credentials missing). It distinguishes itself from siblings like authenticate and toolkit_info by focusing on status/URLs rather than actions or general 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 implies this is for checking connection status before or after authentication, but it doesn't explicitly say 'use this before authenticating' or 'when you need to know provider connection status.' It doesn't mention when not to use it or alternatives beyond the sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
marketplaceAInspectThe official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/ link that opens without login.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| action | No | search | |
| mcp_id | No | ||
| message | No | ||
| tool_id | No | ||
| arguments | No | {} | |
| immediate | No | ||
| tier_slug | No | ||
| prompt_body | No | ||
| prompt_slug | No | ||
| prompt_tool | No | ||
| prompt_vars | No | {} | |
| conversation | No | [] | |
| prompt_title | No | ||
| request_name | No | ||
| cancel_reason | No | ||
| cancel_comment | No | ||
| prompt_targets | No | ||
| report_context | No | ||
| prompt_category | No | ||
| request_details | No | ||
| prompt_description | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description clearly discloses behavioral nuances beyond annotations: invoke works for non-installed MCPs, returns connect/checkout links, and writes require owner/admin. It also explains one-off vs permanent installations, directly addressing security and side effects. No contradiction with annotations; readOnlyHint=false is consistent with the described mutating actions.
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 front-loaded with the tool's purpose and core flow, then systematically covers usage, permissions, and sub-features. While it is long (due to the tool's breadth), every sentence adds value—no redundancy or filler. The organization is logical, though it could benefit from brief section breaks for readability.
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 full lifecycle of the tool: discovery, description, invocation, installation, billing, bug reporting, and prompt library. It explains return behaviors (connect/checkout links, profile details, list_tools output) and authorization requirements. With no output schema, it adequately compensates by describing expected results and edge cases.
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?
Although schema coverage is 0%, the description adds meaning by explaining the role of key parameters like action, query, mcp_id, tool_id, and arguments in the core flow. It also describes prompt-related parameters (prompt_slug, etc.) via the prompt library section. However, it does not directly address many parameters (limit, immediate, tier_slug, conversation, etc.), so it is not fully comprehensive.
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 identifies the tool as the official mcp.ai marketplace, distinguishing it from sibling tools like toolkit_info and authenticate. It provides a specific verb+resource structure and explicates the core flow of search/describe/invoke, which is unique to this tool.
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 explicitly states when to use each action, such as 'prefer invoke for a single/occasional use' and 'Use install only to make an MCP PERMANENT in the active toolkit.' It also contrasts invoke vs install and describes the workflow for prompt library, giving clear alternatives and conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
report_bugAReport a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| message | Yes | ||
| conversation | No | [] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover idempotency (idempotentHint=true) and non-destructiveness (destructiveHint=false), so the description need not repeat those. It adds the useful behavioral detail that the conversation array should be included for reproduction. However, it does not disclose what happens after reporting, any external side effects, or whether a confirmation is 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 two short sentences with no filler. It front-loads the purpose and immediately provides the key usage instruction about the conversation array.
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 feedback/reporting tool with three string parameters and no output schema, the description covers the core behavior and the most important parameter. It could be more complete by clarifying what should go in 'message' and 'context', but overall it is sufficient for basic invocation.
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 does add meaning for the 'conversation' parameter by explaining it should contain recent messages for reproduction. However, it does not clarify the semantics of 'message' (the required parameter) or 'context', leaving two of three parameters under-described.
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 verb and resource: 'Report a bug, missing feature, or send feedback.' This is specific and differentiates it from sibling tools like authenticate, connect, and show_version, which serve unrelated 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?
The description gives clear context for when to use the tool: whenever the user wants to report a bug, missing feature, or provide feedback. It does not explicitly name alternatives or exclusions, but the sibling tools are distinct enough that 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.
show_versionAShow the current MCP platform and adapter versions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which cover the safety profile. The description 'Show...' is consistent with these hints but does not add additional behavioral context (e.g., return format or side effects). Since annotations carry the transparency burden, this is acceptable.
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 sentence, front-loaded, zero filler. Every word earns its place, and the structure is appropriate for a version-query 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?
Given no parameters, no output schema, and a simple query purpose, the description fully covers what the tool does. The annotations handle safety, and the description is sufficient for an agent to understand when and how to invoke 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 the baseline is 4. The empty input schema requires no parameter documentation, and the description does not need to add any parameter semantics. This score aligns with the 0-param 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 resource ('current MCP platform and adapter versions'), clearly distinguishing the tool's purpose from sibling tools like 'toolkit_info' which might provide broader info. The name 'show_version' reinforces this.
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 usage is implied by the description: if you need to check platform/adapter versions, this is the tool. However, there is no explicit mention of when to use it over alternatives, nor any exclusions. Minimal guidance but adequate for a simple read-only utility.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
toolkit_infoAReturns the current toolkit state: installed MCPs, their connection status, the accounts connected to each one, and how many catalog tools each exposes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to restate that. The description adds value by detailing what state is returned, but it doesn't go beyond that into other behavioral aspects like auth requirements or performance implications. It's consistent with annotations and adds modest contextual 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 a single, well-structured sentence that immediately states the action and then uses a colon to list exact return contents. No wasted words; every part 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?
With no output schema and a simple informational purpose, the description completely enumerates what the tool returns: installed MCPs, connection status, connected accounts, and catalog tool counts. Nothing critical is missing for an agent to decide to call 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 schema burden. According to the rubric, a baseline of 4 is appropriate when no parameters need documentation, and the description doesn't need to compensate for any coverage gaps.
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 what the tool does: returns the current toolkit state, listing specific contents (installed MCPs, connection status, accounts, catalog tool count). It uses a specific verb and resource, distinguishing it from siblings like show_version 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 that this is a read-only informational tool for inspecting toolkit state, implying when to use it. It does not explicitly mention alternatives or when-not-to-use cases, but the intent is evident given the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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