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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate idempotent, non-destructive, non-read-only behavior; the description adds useful context about token permanence and session-only fallback. It does not disclose the exact response when a token is passed, but this is a minor gap for an authentication 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?
The description is dense and informative, with three sentences covering flow, permanent setup, and session-only usage. It is slightly run-on, but every clause adds necessary operational detail.
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-parameter tool with no output schema, the description adequately covers the authentication workflow, both login modes, and the no-args behavior. Explicit return/error behavior is missing, but the low complexity and annotations make the description sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully compensates by explaining the optional token parameter is a pasted JWT for session-only login, and that calling with no arguments returns the browser login link. This gives the agent complete actionable semantics for the single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: authenticating with MCP.AI by logging in via browser and obtaining/using an access token. It provides concrete invocation details, but it does not explicitly distinguish itself from the sibling 'connect' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance on two usage modes: permanent config-based authentication via Authorization header versus session-only login by pasting a token. It does not mention when not to use the tool relative to siblings, but the mode-selection guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools serve distinct purposes (auth, consultation, marketplace, system info), so an agent can usually tell them apart. However, 'authenticate' and 'connect' have overlapping roles regarding credentials, and 'marketplace' is a catch-all that blurs the boundary between platform and domain tools.
Names mix verbs (authenticate, connect), noun phrases (marketplace, toolkit_info), and a long snake_case domain call (detran_df_veiculo_mobile_consultar). No consistent verb_noun pattern, and the naming style shifts across tools.
7 tools is a reasonable count, but the server's stated purpose (DETRAN vehicle consultation) is served by only one domain tool; the other six are generic platform utilities that belong to a broader MCP toolkit. This scope mismatch makes the count feel inflated and unfocused.
For a domain-specific server, the surface is nearly empty: only one consultation operation with no related CRUD or auxiliary functions (e.g., history, multiple vehicle types, correction). The remaining tools are unrelated to the domain, leaving obvious gaps for real vehicle queries.