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 provide idempotent, non-destructive hints, and the description adds meaningful context: browser login flow, two-auth-mode behavior, permanent vs session-only token, and no-args behavior. It does not disclose exact outcomes after submitting a token, but the added context exceeds what annotations alone offer.
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 front-loaded with purpose and explains both workflows in two sentences. All clauses contribute meaning, though the 'MCP.AI for IDE agents (Cursor, etc.)' preamble could be slightly tightened.
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 auth tool with no output schema, the description covers the primary usage paths: browser login, config header, token call, and no-args link generation. Missing return/error semantics after token submission are a minor gap but do not undermine usability.
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 schema has only one optional token parameter with zero description coverage. The description compensates by explaining the token is a JWT used for session-only login and that omitting it returns a login link, which is sufficient for this simple 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 authenticates the user to MCP.AI for IDE agents through browser login and token exchange. It specifies two concrete usage modes: permanent config header or session-only token, which distinguishes it from sibling tools 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 explicitly explains when to use the permanent config-header approach versus the session-only token call, and clarifies that calling with no arguments returns the login link. However, it does not mention alternatives or when-not-to-use the tool.
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 purpose, from ticket lifecycle operations (create, cancel, close, update) to list/get actions for various entities (clients, desks, contracts, etc.). Even similar tools like create_ticket_answer vs create_internal_communication are well-differentiated in their descriptions, with explicit cross-references. The platform-level tools (authenticate, marketplace, etc.) are also unambiguous.
All tiflux-domain tools follow a consistent pattern: tiflux_ + verb (create, get, list, update, cancel, close) + noun (ticket, client, desk, appointment). Proper nouns like tiflux_tickets_feedback_report deviate slightly but still align with the tiflux_ prefix and use a clear descriptive name. Platform tools are uniformly simple verbs (authenticate, connect) or noun phrases (toolkit_info), maintaining internal consistency.
With 36 tools, the server is well above the 25-tool threshold that starts to feel overwhelming. While each tool appears to serve a distinct purpose within a complex ticketing system, the sheer volume could burden an agent with too many options, increasing selection difficulty and cognitive load. A more curated set (e.g., 15-20) might suffice for most workflows.
The tool surface covers the full ticket lifecycle (creation, listing, details, updates, cancellation, closure) plus supporting entities (clients, desks, stages, priorities, SLAs, knowledge base, requestors, technical groups, appointments, files, feedback reports). The inclusion of bulk operations and multi-id fetches indicates thorough consideration of agent needs. No critical gaps are apparent for the stated purpose of ticketing and helpdesk management.