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?
Discloses important behaviors: browser login, permanent vs session-only connections, and no-args link generation. This complements the annotations (e.g., idempotentHint=true) without contradicting them, adding useful context beyond the structured metadata.
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?
Description is a single compact sentence that conveys multiple usage modes without excessive verbosity. The product context 'MCP.AI for IDE agents' adds minor background but does not waste space.
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 the simple schema (one optional string param) and no output schema, the description covers the core workflow effectively: login, token acquisition, and token usage. It omits only minor details like success/error responses, which are not essential for a straightforward 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 description coverage is 0%, so the description carries the burden. It explains the optional 'token' parameter as a JWT obtained from browser login and clarifies the no-args case, giving meaning beyond the bare schema definition.
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 IDE agents via browser login and access token. It details the login flow and distinguishes itself from siblings like 'connect' and 'marketplace' by focusing specifically on authentication.
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 provides explicit usage scenarios: add token to config for permanent connection, paste token for session-only login, or call with no args to get the link. It does not mention alternative sibling tools, but the guidance is clear and practical.
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.
The tiny_* tools are clearly distinct by resource and action, but the generic platform tools blur together: marketplace also handles report_bug, install/uninstall, token/connect links, and toolkit state, overlapping with authenticate, connect, report_bug, and toolkit_info. An agent could easily call marketplace for something a dedicated tool already covers, or confuse connect with authenticate.
The Tiny ERP tools follow a clean verb_noun pattern (tiny_list_orders, tiny_get_product, tiny_create_contact), but the platform tools use short unprefixed verbs (authenticate, connect, marketplace, report_bug, show_version, toolkit_info). Two internally consistent naming schemes coexist, so the overall set feels mixed rather than chaotic.
At 20 tools, the count is borderline heavy and mixes two distinct concerns: Tiny ERP operations and mcp.ai platform administration. The ERP tools are each reasonably scoped, but the generic tools could likely be consolidated, especially marketplace, which already absorbs several capabilities.
The ERP surface is heavily read-oriented: contacts, orders, invoices, products, payables, and receivables all support list/get, but only contacts have a create operation. There are no update/delete operations for any entity and no way to create orders, invoices, or products, which creates significant dead ends for real business workflows.