DETRAN BA: Veículo
Server Details
DETRAN BA: Vehicle, official-source lookup. Platform-hosted, pay per query with prepaid credit.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- mcp-dir/detran_ba_veiculo-mcp
- GitHub Stars
- 0
- Server Listing
- detran_ba_veiculo-mcp
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 provide idempotentHint=true and destructiveHint=false, which are helpful, but the description adds important context: it explains the two login modes, the token lifespan (permanent vs session-only), and how the token can be stored in config. It lacks explicit mention of side effects (e.g., modifying config), but the idempotent hint and the lack of destructive hint suggest safety. The description goes beyond the annotations by detailing the token handling.
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 tool's identity and use case. Every clause adds value: the target audience, the login process, the permanent vs session distinction, and the invocation syntax. No waste; it reads like a compact guide.
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 (auth, token management, config vs session) and the lack of output schema and nested objects, the description is surprisingly complete. It covers all essential behaviors: how to authenticate, where to put the token, the two modes, and how to invoke the tool. It might not explain the response, but for an auth tool, the primary outcome (connection established) is implied. The description leaves little 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?
The schema has one optional optional string parameter 'token' with no description, so schema coverage is 0%. The description compensates well: it explains the token's purpose ('{ token: "<jwt>" }') and specifies that calling with no args returns the link. It also gives the context of the token (JWT) and where to obtain it (browser). The only gap is that it does not describe the response format, but with no output schema, that burden is on the description; it doesn't say what the function returns, but the purpose is clear.
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 ('authenticate'), resource ('MCP.AI for IDE agents'), and the mechanism (browser login, token copy, config header vs session token). It distinguishes from siblings by providing explicit login flow and alternatives, though it doesn't reference sibling names directly but the purpose is unmistakable.
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 and how-to-use: 'log in in the browser, copy the access token' and offers two modes (permanent via config header, session-only via token param). It also says 'call with { token: "<jwt>" } after the user pastes, or with no args to get the link.' This is far more than minimal guidance, though it does not explicitly say when not to use it, but for an authentication tool, the context is clear.
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 indicate read-only and idempotent behavior. The description supplements this by explaining response states (authenticated:true with empty pending[], or connect_url and per-install URLs when credentials are missing). This adds meaningful context about what the tool returns without contradicting 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?
Two sentences, no fluff. The first sentence states the core function; the second explains the conditional output. Every clause 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 tool with no parameters and no output schema, the description fully explains the two relevant return scenarios. It covers the essential states without unnecessary detail, making it complete for all practical purposes.
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 zero parameters, and schema coverage is trivially 100%. Per the rubric, the baseline is 4, and there is no parameter information needed. The description adds no parameter details (nor should it) since there are none.
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: 'Returns connection status and URLs.' It uses a specific verb and resource, and the title 'Connection Status' reinforces the purpose. However, it does not explicitly distinguish itself from sibling tools like 'authenticate', relying on context rather than naming alternatives.
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 behavior-based context ('When all providers are connected... When credentials are missing...') which implies when this tool is useful, but it does not explicitly state when to use or avoid it compared to siblings. No alternative tools are named, so the usage guidance is indirect.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
detran_ba_veiculo_consultarARead-onlyIdempotentInspect
DETRAN BA: Veículo, 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 |
|---|---|---|---|
| chassi | No | ||
| renavam | No | ||
| login_cpf | Yes | ||
| login_senha | Yes | ||
| pkcs12_cert | No | ||
| pkcs12_pass | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and non-destructive; the description adds that it is hosted without platform credentials, paid per query, and that data is not confidential with LGPD responsibility, providing useful operational context.
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 reasonably concise, opening with the main purpose and adding payment and legal context in three sentences; it 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?
With no output schema and no parameter details, the description leaves out critical usage information such as how to query by chassi vs renavam and what the response will contain; it provides compliance/payment context but lacks operational completeness.
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% and the description does not mention any of the six parameters (chassi, renavam, login_cpf, login_senha, pkcs12_cert, pkcs12_pass), leaving their purpose and usage unexplained.
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 'DETRAN BA: Veículo, consulta em fonte oficial' and elaborates that it queries official Brazilian sources, distinguishing it from sibling platform utilities like authenticate and 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 context of a vehicle consultation for DETRAN BA with prepaid credit is clear, but no explicit 'use when' or alternatives are given; however, the sibling list contains only generic tools, so the usage is evident.
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?
Beyond annotations (readOnlyHint=false, openWorldHint=true), the description discloses auth requirements ('must require workspace owner/admin'), the side-effect of invoke (one-off install without bloating the tool list), and the connect/checkout fallback links when credentials or payment are needed. No contradiction 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?
The description is dense and long (over 250 words) but reasonably structured with 'Core flow:' and 'KEY:' markers. It front-loads the primary purpose, yet many sentences are run-on and could be trimmed. Acceptable for the tool's complexity, but not concise.
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 an output schema, the description explains return types for several actions (describe returns a profile, invoke returns links, publish_prompt returns a slug link). It covers permissions, fallbacks, and the prompt library. Some actions like search or list_tools lack explicit return descriptions, though their purposes are clear.
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 23 parameters and 0% schema coverage, the description compensates by explaining the core action flow and mentioning key parameters (action, mcp_id, tool_id, arguments) in context. However, it does not systematically cover parameters like tier_slug, prompt_vars, cancel_reason, or immediate, leaving many parameters underspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is the official mcp.ai marketplace for discovering and running MCPs, with a specific core flow (search → describe → invoke). It names concrete operations like 'find an MCP that does X' and 'consulta um CPF', distinguishing it from sibling tools that handle authentication or specific consultancy.
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 explicit when-to-use guidance: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', and distinguishes invoke from list_tools. It also notes that report_bug/request_mcp are for feedback/new builds, and that writes require workspace owner/admin.
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 declare non-readOnly, non-destructive, and idempotent behavior. The description adds minimal behavioral context, only instructing to include conversation for reproduction. It doesn't describe side effects or dependencies, but given annotations, that's 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?
The description is a single, concise sentence that front-loads the purpose and includes the key instruction without 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 the primary purpose and one usage hint, but leaves 'context' undefined and doesn't clarify the format of the conversation parameter (the schema says string, not array) or the expected outcome. A bit more detail would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the purpose of the 'conversation' parameter (for reproduction) and implies 'message' is the feedback text. However, it doesn't clarify the 'context' parameter at all. With 0% schema coverage, the description partially compensates but is incomplete.
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: reporting bugs, missing features, or feedback. It distinguishes from sibling tools (authentication, vehicle consultation, etc.) by 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 provides a clear context for use (when you encounter a bug or want to send feedback) and includes a specific instruction to include the conversation array for reproduction. It doesn't explicitly mention alternatives or exclusions, but the context is clear 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.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds that it shows both platform and adapter versions, which is slightly more specific than the tool name. No additional context about output format or auth is provided, but given the read-only nature, 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?
The description is a single, concise sentence with no unnecessary words. It is front-loaded and directly communicates the tool's function.
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 tool with strong annotations, this description is fully complete. It provides all needed information for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing to explain. Per the rubric, 0 params earns a baseline of 4, and the description does not need to add anything beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action 'Show' and the specific resource 'current MCP platform and adapter versions'. This distinguishes it from sibling tools like authenticate or connect, which have different 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 usage is implied: one would call this to check versions. However, there is no explicit statement about when to use it instead of alternatives or any exclusions. Since the tool is trivial and unique, this is adequate but not exemplary.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that it returns connection status and account info, which adds context about the kind of data returned, but doesn't go beyond what annotations imply. Without output schema, it moves to the description, but it still lacks detail on potential latency or whether it triggers network calls.
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 is concise yet comprehensive, enumerating exactly what data is returned. Every phrase contributes value 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 zero parameters and only annotations describing safety, the description effectively explains what the tool returns. It's a simple state inspection tool, so the description is sufficient. However, it could mention if the state is live or cached, but that's a minor gap given the simplicity.
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 zero parameters, there is nothing to explain, and the schema coverage is 100%. A baseline of 4 is appropriate because no parameters exist, so the description doesn't need to add parameter semantics.
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, enumerating specific contents: installed MCPs, connection status, accounts, and catalog tool counts. This is specific and distinguishes it from sibling tools like connect or authenticate, which focus on actions rather than 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 implies it's for inspecting the current toolkit state, which is clear given the context of siblings like connect and authenticate. It doesn't explicitly state when NOT to use it, but the read-only nature and clarity of purpose make the intended usage obvious.
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
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
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Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceEnables querying DETRAN BA vehicle licensing information from the official source via a read-only MCP server. It works with any MCP client over HTTP using pay-per-use credits.MIT
- AlicenseNot gradedqualityCmaintenanceA hosted read-only MCP server that enables querying IPVA (vehicle property tax) information from the official SEFAZ Bahia source, with prepaid credit-based usage and no platform credentials.MIT
- AlicenseNot gradedqualityCmaintenanceEnables querying vehicle licensing information from DETRAN AL (Alagoas, Brazil) via an official read-only MCP tool, with pay-per-use credit system and no credentials required.MIT
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
The actual DETRAN query tool is unique, but several tools overlap in the auth/connection/status space: authenticate, connect, toolkit_info, and marketplace all describe related login, connection, and available-tool responsibilities. An agent could easily call the wrong one when trying to determine connection status or perform a one-off query.
Naming is inconsistent: English verbs, underscore-separated English nouns, noun-style platform names, and a Portuguese object-verb form are all mixed together. There is no consistent verb_noun or domain_suffix pattern across the tool set.
Seven tools is not too many in absolute terms, but only one tool is about the server's apparent DETRAN domain. The rest are generic platform/auth/marketplace utilities, making the set feel lopsided and not purpose-built for vehicle consultations.
For a read-only public-data query, a single consultation tool could be acceptable, but the domain surface is still very thin. There are no related operations for different query types, no explicit failure-handling mechanisms, and no other DETRAN-related capabilities to help an agent learn what is possible.