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 discloses that a config-header approach yields a non-expiring connection, while passing a token is session-only, and that no args returns a login link. This adds meaningful behavioral context beyond the annotations (idempotentHint, destructiveHint, readOnlyHint). No contradiction with annotations is present.
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 exceptionally concise, using three focused sentences to cover purpose, best practice, and alternative usage. Every sentence contributes actionable information and the structure is front-loaded with the tool's context.
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 simple tool with one optional parameter and no output schema, the description covers the core workflow, parameter semantics, and important behavioral nuances. It does not describe return values or error handling, but these are not essential given the tool's simplicity and the strength of the other information provided.
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 only defines 'token' as a string with no description. The description explains the token is a JWT, provides an example call format ({ token: '<jwt>' }), and clarifies that the parameter is optional since calling with no args yields a link. This fully compensates for the 0% schema description coverage.
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 an MCP.AI server for IDE agents via browser login and access token. It specifies the resource (MCP.AI) and the action (log in, copy/paste token), but does not explicitly differentiate from the sibling tool 'connect', so it doesn't fully earn a 5.
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?
It gives explicit guidance for two usage scenarios: adding the token to config for a permanent connection, or passing it as a parameter for a session-only login. It also notes calling with no args to get the link. However, it does not mention when to use this tool instead of siblings like 'connect', so it lacks explicit exclusions or alternatives.
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: renda_estimada_consultar, report_bug, show_version, and toolkit_info each have unique purposes. However, authenticate and connect overlap around authentication/connection status, and marketplace and toolkit_info share some visibility into installed tools, causing minor ambiguity.
Tool names are inconsistent: some are English verbs (authenticate, connect, report_bug, show_version), some are English nouns (marketplace, toolkit_info), and the sole domain-specific tool is in Portuguese with an awkward noun_noun_verb structure (renda_estimada_consultar). No clear naming pattern emerges.
The count of 7 tools is within a reasonable range, but the server's stated purpose is socioeconomic level and income estimation, while only one tool addresses that domain. The rest are generic platform utilities, making the set feel misaligned and somewhat bloated for the claimed focus.
For a server supposedly dedicated to income estimation, there is only a single 'consult' operation and no supporting tools (e.g., batch lookup, validation, or explanation of data sources). The large number of unrelated utility tools does not compensate for the lack of domain coverage, leaving significant gaps for agents seeking comprehensive socioeconomic data.