Returns connection status and URLs. When all providers are connected, returns authenticated:true and empty pending[]. When credentials are missing, returns connect_url for the toolkit and per-install URLs.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 already declare readOnlyHint=true and idempotentHint=true. The description adds meaningful behavioral detail: it explains the output states for connected vs. missing credentials, including the exact fields returned. This helps the agent anticipate results, though it doesn't cover all possible states.
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 two sentences long with no filler. It front-loads the primary purpose ('Returns connection status and URLs') then elaborates on behavior.
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 tool's simplicity (no params, no output schema), the description covers what the tool does and its key output variations. It's sufficient for an agent to know when and how to invoke it.
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 tool has zero parameters, so the description has no need to explain parameter syntax. The schema is empty and the baseline for 0 params is 4. The description adds no parameter info, but none is needed.
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 returns connection status and URLs, with specific behavior for two conditions. This distinguishes it from sibling tools like 'authenticate' which likely initiates authentication. No ambiguity.
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 implies the tool is for checking connection status, but doesn't explicitly state when to use it versus 'authenticate' or other siblings. No alternatives or exclusions are mentioned. It's functional but lacks explicit guidance.
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 have clearly distinct purposes (auth, query, status, feedback, version, toolkit state). However, marketplace is a mega-tool that includes report_bug functionality, creating some overlap with the dedicated report_bug tool, and connect/toolkit_info both relate to connection status.
Naming is inconsistent: camelCase (connect, report_bug), snake_case (cgu_consultar), and mixed patterns overall. Also, cgu_consultar is Portuguese while other tool names are English, breaking a predictable convention.
Seven tools is a reasonable count, but the marketplace tool is overloaded with many sub-actions (search, describe, invoke, install, subscribe, etc.), making it feel like a large number of hidden tools bundled into one.
The server's primary domain is CGU data consultation, but only one tool (cgu_consultar) addresses that domain. The rest are platform utilities (auth, marketplace, version, etc.), leaving obvious gaps such as different consultation types, data explanations, or batch operations.