Banco Central do Brasil (BCB): Cotações, official-source lookup. Platform-hosted, pay per query with
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Average 4.1/5 across 7 of 7 tools scored.
Most tools are clearly separated (report_bug, show_version, toolkit_info), but authenticate and connect overlap around credentials/connection status, and marketplace's invoke capability could be confused with the BCB query tool for running data lookups. The line between 'using the platform' and 'querying BCB data' is not crisp.
Naming conventions are mixed: bare verbs (connect), English verb_noun pairs (report_bug, show_version), nouns (marketplace, toolkit_info), and one Portuguese noun_verb construction (bcb_cotacoes_consultar). There is no consistent pattern across the set.
Seven tools is a reasonable count by number, but six of them are generic platform/utility tools (marketplace, connect, authenticate, etc.) with only one BCB-specific tool. The set is not tightly scoped to the server's stated purpose of BCB quotes.
For the actual BCB domain, only one query tool exists; it may cover basic quote lookups but there is no dedicated tool for enumerating available series, currencies, or historical ranges. The core read path exists, but the domain surface feels thin.
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?
Annotations provide idempotentHint: true and destructiveHint: false, which are helpful. The description adds transparency about the authentication flow: it mentions that the token can be added to config for a permanent, non-expiring connection, or used session-only. It does not contradict annotations (idempotentHint true aligns with being non-destructive). It lacks details on what happens exactly when called with invalid token, but overall sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat lengthy but covers essential information. It is front-loaded with the target audience and method. There is no wasted repetition, but it could be more concise (e.g., 'MCP.AI for IDE agents' could be trimmed). Overall, every sentence adds value.
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 that there is no output schema and only one optional parameter, the description is quite complete. It explains the two modes of use (permanent config vs session-only), the process of obtaining the token, and the fallback of getting the link. With idempotentHint true and no out schema, the description covers the necessary context for an agent to guide the user.
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. The description explains the 'token' parameter semantically: it accepts a JWT pasted by the user for session-only login, and it also clarifies that calling with no args returns the link. This adds meaning beyond the schema's bare 'token' definition. It could be more explicit about token format, but that's minor given the JWT hint.
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 purpose: to authenticate the user for MCP.AI IDE agents by either obtaining a browser login link or accepting an access token. It distinguishes itself from sibling tools by focusing on authentication, which is unique among the listed siblings (e.g., connect is likely a different connection tool, but not named). The verb 'authenticate' plus the context of browser login makes the purpose specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: it tells the user to log in via browser, copy the access token, and then either add it to the server config for permanent access or call with a token for session-only login. It also clarifies when to call with no args to get the link, and mentions the alternative of adding the token to the config as a header. This is clear when-to-use and how-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bcb_cotacoes_consultarABanco Central do Brasil (BCB): Cotações, 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 |
|---|---|---|---|
| data | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful context beyond that: no credentials needed, cost model (prepaid credit per query), data classification (non-confidential), and LGPD compliance responsibilities. These operational details help the agent set expectations.
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-loads the core purpose, and uses three additional sentences to add operational and legal context. The LGPD disclaimer could be considered slightly superfluous but does not harm clarity.
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 having a simple signature (one optional string parameter, no output schema), the description omits any explanation of the parameter's meaning or expected input. This is a critical gap that prevents an agent from invoking the tool correctly. The legal and payment details don't address invocation requirements.
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 has one parameter ('data') with 0% description coverage, and the description gives no explanation of what 'data' means or how to format it. With no parameter documentation, the description fails to compensate for the schema's lack of semantic detail, leaving the agent unable to determine valid input.
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 queries official Banco Central do Brasil exchange rates ('Cotações, consulta em fonte oficial'), using a specific verb ('consultar') and resource. It also distinguishes itself from the generic platform sibling tools (authenticate, connect, marketplace, etc.) by focusing on BCB data.
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 operational context: it queries an official source, requires no platform credentials, and is paid per query with prepaid credit. This helps an agent understand when to use it, though it doesn't explicitly mention when not to use it or name alternative tools.
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, so the agent knows this is a safe, non-mutating operation. The description adds useful behavioral context by explaining the output format under different states (authenticated:true and empty pending[], or connect_url for missing credentials). However, it does not detail the return structure beyond that, and since annotations cover the safety profile, a score of 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 extremely concise: two sentences that efficiently convey the tool's purpose and the key behavioral variance. There is no redundancy or filler. Every sentence adds useful information, making it an excellent example of conciseness for a zero-parameter 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 the tool's simplicity (0 parameters, no output schema) and the strong annotations (readOnlyHint, idempotentHint), the description provides sufficient context: it explains the states of the connection and the meaning of connect_url. It could mention what 'authenticated:true' means for the pending array or list the providers, but those details are minor. Overall, it is complete for its intended use.
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 no parameters, so the description does not need to explain any. The parameter_semantics dimension defaults to a high score because there is no parameter schema to augment. The provided description adds no parameter info, but that is not needed. Thus, giving a 4 is justified as there is no ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it returns connection status and URLs. It specifies what the output looks like under different conditions (all providers connected vs missing credentials). While it does not explicitly contrast with sibling tools, its function is distinct enough from the listed siblings (e.g., authenticate, marketplace) to be clear.
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 this tool (when checking connection status) but does not explicitly state when not to use it or how it differs from the 'authenticate' sibling. It would benefit from saying something like 'Use this to check if setup is complete; use authenticate to initiate connection.' However, the context is clear enough for basic usage.
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?
Beyond the annotations (readOnlyHint false, openWorldHint true), the description reveals critical behavioral traits: invoke runs MCPs without installation, returns connect/checkout links for auth or payment, and writes require workspace owner/admin. It also clarifies that search/describe flag installation status, adding substantial context not present in 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 dense paragraph, but it is well-structured and front-loaded with the most important purpose and flow. Each sentence adds value, covering every action and key behaviors. While lengthy, the complexity of the tool justifies the detail; it is not redundant.
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 (14 actions, 23 parameters, no output schema), the description covers the main flow, permissions, auth/payment handling, and the separate prompt library. It explains all actions and their purposes, though it does not detail every parameter's syntax or format. Overall, it is sufficiently complete for correct invocation in most scenarios.
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 action enum and the core parameters like mcp_id, tool_id, and arguments implicitly through the flow. However, it does not detail many parameters (e.g., limit, query, immediate, tier_slug, prompt_vars), leaving gaps for users to infer from examples. It adds meaning beyond the schema but not exhaustively.
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, explaining it is both a catalog of MCPs/tools and the means to run them. It provides a specific core flow (search→describe→invoke) and distinguishes itself from sibling tools by its unique scope, making it distinct from utilities like authenticate or show_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers explicit guidance on when to use different actions: prefer invoke for one-off usage, use install only for permanent inclusion, and explains that list_tools shows callable tools. It also contrasts alternatives (invoke vs install) and flags permission requirements for writes, giving clear context for selecting actions.
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 convey safety profile (not read-only, idempotent, not destructive). The description adds the useful context that conversation data is needed for reproduction, but it does not disclose response behavior, persistence, or any other side effects beyond the obvious reporting action.
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-load the purpose and provide the key usage note without filler. Every sentence contributes functional guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple no-output-schema feedback tool, the description covers purpose and a key input requirement. It is adequate but leaves gaps around the context parameter, expected response, and exact conversation formatting.
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 (include recent messages) but leaves context unexplained and only implies that message holds the report text; the serialized string format for conversation is also not clarified.
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 a reporting channel: 'Report a bug, missing feature, or send feedback.' It uses a specific verb and resource and is distinct from sibling tools like authenticate, marketplace, and show_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It enumerates the intended use cases (bug, missing feature, feedback) and instructs the user to include the conversation array for reproduction. It does not explicitly state exclusions or alternatives, but none of the sibling tools overlap with bug reporting.
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 read-only and idempotent behavior, and the description's 'Show' is consistent. It adds minimal extra context by specifying 'current' and 'platform and adapter' versions, but does not disclose any further behavioral details such as performance or external dependencies.
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 with no wasted words. It efficiently conveys the full purpose of the 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 the tool's simplicity (zero parameters, no output schema), the description is complete enough for an agent to understand what it returns and when to use it. The annotations cover safety, making this a self-contained and adequate description.
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 vacuously 100% and no parameter explanation is needed. Per the baseline for 0 params, a score of 4 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 uses a specific verb ('Show') and a clear resource ('current MCP platform and adapter versions'), unambiguously stating the tool's function. It distinguishes itself from sibling tools by focusing on version information rather than generic 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?
No guidance is provided for when to use this tool versus alternatives like toolkit_info. The description only states what it does, not when it should be chosen over siblings, leaving the agent to infer usage from context.
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 cover readOnly, idempotent, and destructive hints. The description adds specific behavioral context about what the returned state includes (connection status, accounts, catalog counts), which goes beyond the annotations and helps the agent set expectations.
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 that efficiently lists the key elements of the returned state without redundancies. It is front-loaded with the action 'Returns' and immediately itemizes the content.
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 zero parameters foster no output schema, the description fully specifies what the tool returns: installed MCPs, connection status, connected accounts, and catalog tool counts. No further detail is needed for a simple read-only state inspection 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 tool has zero parameterschers, so there are no parameter semantics to clarify. The description completely fulfills the requirement without needing to compensate for missing schema details.
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 verb 'Returns' and the resource 'current toolkit state' with specific details (installed MCPs, connection status, accounts, tool counts). It distinguishes itself from siblings like 'connect' and 'authenticate' by focusing on state 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 provides clear context for when to use the tool (to inspect current toolkit state) and implies its role among siblings (e.g., connect/authenticate for modifications). It lacks explicit exclusions or named alternatives, but the purpose is unambiguous.
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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