INPI: Marcas
Server Details
INPI: Marcas, official-source lookup. Platform-hosted, pay per query with prepaid credit.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- mcp-dir/inpi_marcas-mcp
- GitHub Stars
- 0
- Server Listing
- INPI: Marcas
Available Tools
7 toolsauthenticateAIdempotentInspect
MCP.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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate idempotentHint=true, and description explains it either returns a link or sets a session token, adding context on session vs permanent setup. No contradiction; description complements annotations with browser flow 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?
The description is informative but slightly verbose; however, it is well-structured with key info upfront (purpose and audience) and then details the two authentication methods. Each 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?
For a simple one-param tool with no output schema, the description covers the authentication process, both modes, and invocation details. It doesn't specify output format or error handling, but is adequate for the tool's complexity.
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 single 'token' parameter as a JWT, its usage for session-only login, and the alternative of no args to get a link.
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 authenticates for IDE agents, specifying verb 'log in' and resource (access token). It distinguishes from siblings like 'connect' and 'marketplace' by focusing on authentication.
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 provides clear usage context for IDE agents, explains two flows (permanent via config header, session-only via token) and how to invoke with or without the token. It lacks an explicit 'when not to use' but sufficient guidance for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
connectARead-onlyIdempotentInspect
Returns 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 | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds value by specifying response content under different connection states (authenticated:true with empty pending[] vs. connect_url and per-install URLs). This goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and every word contributes. No redundancy, and it directly states the tool's function and conditional behavior.
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 annotations already covering safety, the description fully explains the two main output scenarios. It covers when all providers are connected and when credentials are missing, which is sufficient for a simple status-check 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 no parameters, and the schema is empty, so schema coverage is 100%. The description correctly does not attempt to describe parameters. Baseline for 0 params is 4, and no additional semantics are needed.
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, which is a specific verb and resource. It distinguishes from sibling 'authenticate' by focusing on status rather than the authentication action.
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 about when to expect certain outputs (when providers are connected vs. when credentials are missing), but it does not explicitly name alternatives or when-not-to-use. Still, the behavioral conditions imply appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inpi_marcas_consultarBRead-onlyIdempotentInspect
INPI: Marcas, 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 |
|---|---|---|---|
| ncl | No | ||
| tipo | No | ||
| marca | Yes | ||
| pagina | No | ||
| pedidos_vivos | No | ||
| pesquisa_textual | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds important context: paid per-query with pre-paid credits, hosted on platform without platform credentials, data is not confidential but client is data controller under LGPD. This goes beyond annotations, clarifying cost and legal responsibilities, which is valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise paragraph, front-loaded with the main purpose. It includes necessary context about payment and legal compliance, but could be considered slightly dense. No wasted sentences, but it might benefit from breaking up into structured info.
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 6 parameters, 0% schema coverage, and no output schema, the description is insufficient to fully use the tool. It explains the business model and legal aspects but omits parameter guidance and return value expectations. For a complex query tool, it's incomplete.
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 should compensate for the six parameters, but it only mentions 'marca' implicitly and not even explicitly. No parameter names or meanings are explained. The description is entirely silent on what 'ncl', 'tipo', 'pagina', 'pedidos_vivos', 'pesquisa_textual' mean, leaving agents with bare names.
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 states the tool performs trademark queries in the official INPI source, distinguishing it from generic search tools. It clearly identifies the resource (INPI trademarks) and the action (consult). However, it doesn't explicitly differentiate from potential sibling trademark tools, but siblings provided are unrelated (authenticate, connect, etc.), so no conflict.
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 mentions it's for consulting official Brazilian sources and that it's hosted on the platform with pre-paid credits, implying when to use it (paid queries). However, it doesn't provide explicit guidance on when not to use it or alternatives, though the context of paid credits and official data is useful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
marketplaceAInspect
The 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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond annotations by disclosing that invoke runs MCPs one-off even when not installed, that writes require workspace owner/admin, and that credential or payment gaps result in connect/checkout links. These are behavioral traits not present in the annotation block and are directly useful for the agent's expectations. No contradiction with annotations is apparent.
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 dense but front-loaded with the core flow and key decisions. It packs a lot of necessary information into one paragraph, but the lack of bullets or section breaks makes it harder to scan for specific action semantics. Every sentence contributes value, but structure could be improved for a tool with this many actions.
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 complex 23-parameter, no-output-schema tool, the description is unusually complete: it covers core flow, authentication/payment edge cases, permission requirements, installed status flags, and the prompt library. However, it does not explain the return shapes of search/describe/list_tools in detail, and several action-specific parameters remain ambiguous, so there are still notable 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?
With 0% schema description coverage, the description must compensate for the 23 parameters. It does explain the meaning of key parameters through the flow (action, mcp_id, tool_id, arguments, prompt fields), but many parameters such as limit, immediate, tier_slug, cancel_reason, report_context, prompt_targets, and request_details are left undocumented. It adds significant value but does not fully cover the parameter space.
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 — the in-platform catalog of every MCP/tool, AND the way to run them,' with a specific core flow (search → describe → invoke). It clearly covers the main resource (MCPs and prompt library) but does not explicitly differentiate from siblings like report_bug or toolkit_info, and the marketplace action also includes report_bug, which could cause minor confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: 'prefer invoke for a single/occasional use' and 'Use install only to make an MCP PERMANENT in the active toolkit.' It also explains list_tools, subscribe/cancel, request_mcp, report_bug, and the prompt library flow, making it clear when each action is appropriate relative to the core workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
report_bugAIdempotentInspect
Report 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 | [] |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is not read-only, not destructive, and idempotent. The description adds the purpose of the 'conversation' field for reproduction, but doesn't disclose any side effects or submission behavior beyond 'report'. This is adequate but not rich.
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 sentences with no fluff. It front-loads the purpose and then states the key usage instruction. Every sentence 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?
For a simple 3-parameter tool, the description covers the main purpose and one key parameter but leaves 'message' and 'context' undocumented. It provides no return value information, but since there's no output schema, that's not required. The overall completeness is adequate but has clear 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%, so the description must clarify parameters. It only explains 'conversation' as an array containing recent messages, which also conflicts with the schema typing it as a string. The 'message' (required) and 'context' parameters are not explained at 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 the tool's action: 'Report a bug, missing feature, or send feedback.' This is a specific verb (report) with multiple resource types. It is distinct from sibling tools, which are unrelated.
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 guidance on the expected input: 'Include the conversation array with recent messages for reproduction.' This tells the user what to supply for better bug reproduction. It doesn't mention exclusions, but no alternative reporting tools exist among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
show_versionARead-onlyIdempotentInspect
Show the current MCP platform and adapter versions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns perfectly with the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) and adds the 'current platform and adapter' scope. However, it provides no additional behavioral context beyond what the annotations and name already convey, such as output format 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 a single clear sentence with no redundant wording. It front-loads the action and object directly, making it highly 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?
For a zero-parameter read-only version-check tool with strong annotations, the description is sufficiently complete. There is no output schema, but the expected return—version identifiers—is reasonably inferable. A minor improvement would be specifying what 'adapter' includes, but it is not necessary for correct 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?
The tool has zero parameters, so the description has no parameter semantics to explain. The baseline of 4 applies because no further parameter clarification is needed.
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 names the exact resource ('current MCP platform and adapter versions'), making the tool's purpose immediately clear. It also distinguishes this from siblings like toolkit_info or marketplace, which may provide broader information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives, and no exclusionary conditions are stated. Usage is only implied: call this when version information is needed. For such a simple tool, this is adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
toolkit_infoARead-onlyIdempotentInspect
Returns 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 | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds detail beyond annotations by enumerating the contents of the returned state (installed MCPs, connection statuses, etc.). No side effects are disclosed, but annotations already guarantee readOnly/idempotent behavior, so this is 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 a single, concise sentence that directly conveys the tool's purpose without extraneous words. It is well-structured and easy to parse.
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 lack of parameters and output schema, the description is complete in specifying what the tool returns. It fully covers the tool's functionality within its simple context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; the schema is empty, so no parameter explanations are needed. The description does not introduce any parameter-related ambiguity, aligning with the baseline for zero-parameter tools.
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, specifying the exact information included (installed MCPs, connection status, accounts, catalog tool counts). It is a distinct read-only operation compared to 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 querying current state, and the readOnly/idempotent annotations make it safe to call. It does not explicitly contrast with alternatives, but given its unique info-returning purpose, it is clear when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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Discussions
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TDQS
Several tools occupy overlapping territory: `connect` and `authenticate` both handle connection/authorization state, while `connect`, `toolkit_info`, and `show_version` all report on the platform's status from different angles. The `marketplace` tool is a monolith that internally covers search, describe, invoke, install, subscribe, and prompts, which blurs its boundary against nearly everything else. Only `inpi_marcas_consultar` has an unmistakably distinct purpose.
The naming mixes bare verbs (`connect`), verb_noun pairs (`report_bug`, `show_version`), and a noun-first Portuguese id (`inpi_marcas_consultar`), providing no consistent pattern. It's not fully chaotic since there is some use of snake_case throughout, but the English inversion (`toolkit_info` vs `report_bug`) and the language mix harm predictability.
Seven tools is a reasonable count within the ideal 3-15 band, so the server avoids bloat. The only caveat is that the `marketplace` tool appears to wrap the workload of a dozen separate operations, meaning the count understates the true complexity an agent must navigate.
Given the pay-per-query, prepaid-credit model advertised by `inpi_marcas_consultar`, the absence of any balance, credit, or invoice-related tool is a notable gap that forces agent failures between sessions. The rest of the surface is heavily tilted toward platform plumbing (version, install, billing) rather than domain operations, so the trademark-query use case has coverage of only a single thin `consultar` call.