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 and non-destructive behavior. The description supplements this by explaining the different outcomes of calling with vs without a token, and distinguishes session-only from permanent connections. It does not detail error handling or token storage, but covers the primary behavioral variations.
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 moderately concise, with three sentences. The 'Best' configuration instruction adds context but some may be extraneous for the AI agent's direct tool invocation. Still, it is well-structured and action-oriented.
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 covers the core purpose, usage modes, and parameter semantics. It doesn't describe return values or failure modes, but these are not essential given the simplicity and existing annotations.
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 fully compensates by explaining the 'token' parameter's role (a JWT access token) and the optional behavior when omitted. It explicitly shows usage examples with { token: '<jwt>' } and no-args invocation, which adds complete semantic meaning beyond the bare 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?
The description clearly identifies the tool as an authentication tool for MCP.AI in IDE contexts, using verbs like 'log in' and specifying the resource (access token, browser login). It distinguishes from siblings by focusing on access token handling, making its purpose unambiguous.
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 instructions for both invocation modes (with token for session-only login, without args to get a link) and further clarifies the permanent option via config header. This provides clear when-to-use guidance versus alternative approaches, including an explicit 'Best' recommendation.
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.
Each tool has a clearly distinct role: authentication, connectivity status, credit consultation, MCP catalog, bug reporting, version info, and toolkit state. Even though authenticate and connect both deal with access, their functions are separate and unambiguous.
Tool names are inconsistent: single-word verbs (authenticate, connect), a noun (marketplace), and underscore-separated compounds with varying patterns (credito_pf_consultar, report_bug, show_version, toolkit_info). There is no uniform verb_noun or noun_verb convention.
While seven tools is not excessive, the count is inappropriate for the server's stated purpose of credit analysis. Six of the seven tools are generic platform utilities (auth, connect, marketplace, bug reporting, version, toolkit state) that dilute the domain-specific focus. Only one tool actually performs credit analysis.
The core credit consultation operation is covered by credito_pf_consultar, but there are no additional credit-domain operations. The rest of the tools are platform-level, not domain-specific. The surface feels thin for a server named 'Análise de Crédito PF', leaving no further credit-related capabilities.