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 declare idempotentHint=true, and the description is consistent, showing that calling with the same token is idempotent. The description adds behavioral context beyond the annotations: it explains that calling with no args returns a link, with a token establishes a session, and mentions the permanent config option. This is valuable context not present in the annotations.
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 multi-part sentence with a recommendation ('Best:') and usage instructions. It contains no fluff and is front-loaded with the core purpose, but it is somewhat dense and could be better structured into separate sentences for 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?
The description explains the authentication flow and both call variants, and it mentions the return of a link in the no-args case. However, it does not explicitly state what the tool returns when a token is provided (e.g., success/failure), nor any error behavior. Given that no output schema exists, this is a notable gap.
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 one optional 'token' string with no description (0% coverage). The description compensates fully by explaining that the token is a JWT, how to pass it ({ token: '<jwt>' }), and that it is for session-only login, while permanent auth is via config. This adds significant meaning beyond the raw 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: to authenticate for MCP.AI by either generating a login link (when called with no args) or accepting a JWT token for session-only login. It specifies the resource (MCP.AI for IDE agents) and uses a specific verb (authenticate, log in). It does not explicitly distinguish from sibling tools like 'connect', but the function is unambiguous.
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 guidance on how to use the tool ('Best: add it to this server's config...' and 'Or paste it here...'), but it does not explicitly state when to use this tool vs. alternatives (e.g., 'connect'). The usage context is implied rather than contrasted with sibling tools.
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 distinct function, but 'connect' and 'toolkit_info' both report connection status from different angles, causing mild overlap.
Names mix English and Portuguese, verbs and nouns, with inconsistent patterns (e.g., 'vinculo_empregaticio_consultar' vs 'report_bug'). No consistent verb_noun convention.
Seven tools is a moderate count, but six are generic platform utilities while only one serves the server's stated domain, making the set feel oversized and unfocused.
For the employment query domain, the single consult tool covers the core operation, but lacks any related features like history or multi-query support; platform utilities fill the rest.