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 indicate idempotent and non-destructive behavior. The description adds context about the token flow, permanent vs session-only logins, and mentions returning a link when called with no args. It does not explicitly detail error handling or security implications, but the added context 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 bit dense but each sentence adds essential information. It is structured with 'Best' and 'Or' to separate options, making it readable despite its length. No 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?
For a simple optional-parameter auth tool with no output schema, the description covers usage, token semantics, and both authentication modes. It lacks details on failure scenarios or the exact link format, but is reasonably complete for its 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?
Schema coverage is 0%, so the description carries the burden. It explains the 'token' parameter is a JWT for session-only login and that omitting it yields a login link, fully compensating for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: authenticate an MCP.AI server for IDE agents by logging in via browser and obtaining an access token. It distinguishes between permanent configuration and session-only login, providing specific guidance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains when to use each mode: add token to config for a permanent connection, or paste token for session-only login. It also says to call with no args to get the link, giving clear usage context.
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 have mostly distinct purposes, but connect, authenticate, and toolkit_info overlap in connection/status reporting, which could cause confusion. The credit score tool is clearly unique, but the platform utilities blur together.
Naming mixes verbs (authenticate, connect, report_bug, show_version) with nouns (marketplace, toolkit_info) and includes Portuguese (credito_score_consultar). No consistent verb_noun pattern across the set.
The server is named 'Score de Crédito' but only one of seven tools relates to credit scoring; the rest are generic platform tools. The count is misaligned with the apparent domain, making the set feel padded with infrastructure utilities.
For a credit score server, the surface is severely incomplete—only a single query operation exists, with no history, analysis, or management features. The platform tools do not compensate for the lack of domain coverage.