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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses important behavior: calling with no arguments returns a link, and calling with a token authenticates. Annotations indicate idempotentHint=true and no destructiveness, which align with the description. It adds context about configuration options beyond what annotations provide.
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 single paragraph that mixes multiple instructions and scenarios, making it somewhat dense. Though it front-loads the purpose, better structuring (e.g., separating permanent vs session) would improve readability.
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?
Given one parameter and no output schema, the description covers input behavior and usage scenarios adequately. It explains both call modes (with and without token). Minor gap: no mention of the output format, but overall sufficient for a simple authentication tool.
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 must compensate. It explains the 'token' parameter as a JWT for session-only login, adding meaning beyond the schema's property definition alone. It does not describe the return value, but that is acceptable without an output 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 states the tool's purpose: authenticating for MCP.AI IDE agents by logging in and obtaining an access token. It specifies the verb 'authenticate' and the resource (access token), and distinguishes from sibling tools like librelink_* which are unrelated.
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 explains when to use the tool (for login/authentication) and provides two usage scenarios: permanent configuration via config header or session-only login by pasting a token. It does not explicitly state when not to use or mention alternatives, but sibling tools are clearly unrelated.
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 are clearly distinct by function, but librelink_list_accounts and librelink_list_connections are exact aliases, causing confusion. Some glucose tools have overlapping outputs (e.g., librelink_get_current_glucose vs librelink_get_latest_reading), though descriptions help differentiate them.
CGM data tools consistently use the librelink_ prefix, but authentication, connect, marketplace, report_bug, show_version, and toolkit_info lack the prefix, mixing conventions. This inconsistency makes the tool set feel disjointed.
With 16 tools, the set is slightly larger than needed, but it covers the main CGM functionalities plus platform utilities. The number is still reasonable and manageable, with each tool serving a specific purpose.
The CGM data tools provide comprehensive read-only access (current, graph, stats, logbook, etc.), but there are no write operations (e.g., setting targets) or historical data beyond ~12-14 days. The inclusion of marketplace and configuration tools feels out of scope but does not leave critical gaps for the stated domain.