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 adds valuable context about the non-expiring config-based approach versus session-only token login, and that calling with no args returns a link. It does not fully disclose token storage or response details, but the key behaviors are surfaced.
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 compact, front-loaded with the audience and action, and uses a clear 'Best' / 'Or' structure for alternatives. It is slightly dense and runs multiple instructions into one long sentence, though nothing is wasted.
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 one-parameter authentication tool with no output schema, the description covers the main workflows: obtaining a link, pasting a token for session login, and the recommended permanent config approach. It could mention the response after a successful token call or failure modes, but those are minor gaps.
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 only lists an optional string 'token' with no description, so the description carries full weight. It explains that the token is a JWT to paste after the user provides it and distinguishes that omitting it returns the login link.
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 defines the tool's purpose: authenticating this MCP server with MCP.AI for IDE agents, including the browser-login/link flow and token submission. It is specific and actionable, but it does not explicitly distinguish this tool from siblings like '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?
The description gives explicit when-to-use guidance: 'Best: add it to this server's config as a header' for permanent access, and 'Or paste it here for a session-only login' for tool invocation. It also clarifies exactly when to call with a token versus with no arguments.
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.
authenticate, connect, and toolkit_info all touch connection/auth status, and marketplace also handles credential and billing links, so an agent could easily pick the wrong tool. Only sintegra_pr_consultar, report_bug, and show_version are clearly unambiguous.
The set mixes generic English verbs (authenticate, connect, report_bug, show_version), noun-style tools (marketplace, toolkit_info), and a Portuguese verb_noun domain tool (sintegra_pr_consultar). Snake_case is consistent, but there is no predictable action/resource pattern across the set.
Seven tools is not an unreasonable number, but six of them are generic platform/marketplace utilities while only one is actually SINTEGRA-specific. The count is numerically fine yet imbalanced for a server named after a specific data source.
The core SINTEGRA PR consultation capability is present, and the surrounding platform tools cover auth, connection status, billing, and version reporting. The main gap is the absence of any SINTEGRA-specific batch, history, or status operations, but for a single-query consultation service the surface is workable.