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?
The description explains behavior beyond annotations: calling with no args returns a login link, calling with a token establishes a session-only login, and there is an option for a permanent non-expiring connection via configuration. It does not detail token validation or error cases, but the core behavior is transparent.
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 concise and front-loaded, with three sentences that each provide actionable information. Slightly verbose with the 'best way' recommendation, but no redundant 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?
The description covers the main usage scenarios and expected inputs/outputs (link or session establishment). It does not specify the return value format or post-auth behavior, but given the simple schema and idempotent annotation, the context is largely 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?
The input schema has no parameter descriptions (0% coverage), but the description fully compensates by explaining that 'token' is a JWT to paste after user login, and that omitting it yields a login link. This adds clear meaning to the optional 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 this is an authentication tool for IDE agents, explaining the login flow and token exchange. It distinguishes its purpose from sibling tools by focusing on authentication, though it does not explicitly compare itself to '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?
Provides concrete usage guidance: when to call with a token, when to call with no args, and the preferred permanent configuration via the Authorization header. It does not explicitly mention when not to use this tool versus siblings, but the context is sufficiently 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.
Most tools have clearly distinct purposes: authenticate handles login, beneficios_sociais_consultar queries benefits, others handle platform status and actions. However, 'marketplace' is a multi-purpose tool that combines search, describe, invoke, install, etc., which could be confusing for an agent deciding which action to use.
Tool names show mixed conventions: simple verbs (authenticate, connect, report_bug, show_version), a noun (marketplace), a noun phrase (toolkit_info), and a Portuguese snake_case phrase (beneficios_sociais_consultar). No consistent verb_noun or camelCase pattern is followed.
7 tools is within a typical count, but only one tool (beneficios_sociais_consultar) is domain-specific. The other six are generic platform utilities (marketplace, connect, report_bug, etc.) that do not belong in a server dedicated to social benefits, making the set feel bloated and off-purpose.
The core query tool covers the main need of consulting consolidated social benefits for a CPF, which might be sufficient for a read-only public information service. However, the server lacks any supplementary benefit-related operations (e.g., historical queries, filters) and the platform tools do not contribute to domain coverage, leaving the overall surface thin.