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, idempotentHint, and destructiveHint. The description adds valuable conditional behavior: returns authenticated:true with empty pending[] when all connected, and returns connect_url and per-install URLs when credentials are missing. This goes beyond 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 two sentences, front-loaded with the main purpose, and every sentence adds useful information without redundancy or filler.
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 zero-parameter tool with no output schema, the description covers the key states (all connected vs. missing credentials) and is sufficient for an agent to know what to expect. It doesn't need to explain return values since no output schema exists, but it does.
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 schema is empty and coverage is 100%. Per the rubric, the baseline for 0 params is 4; no parameter information 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 different states. This distinguishes it from sibling tools like 'authenticate', which likely establishes connections rather than checking status.
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 used to check connection status and obtain URLs when needed, but it does not explicitly mention when not to use it or name alternatives like 'authenticate'. Context is clear, but exclusions are missing.
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: restituicao_irpf_consultar is the only domain-specific tool, and the platform utilities (marketplace, report_bug, show_version, toolkit_info) each serve a unique purpose. However, authenticate and connect both relate to connection/auth status, creating slight overlap.
Tool names follow no single convention: some are verbs (connect, authenticate, show_version), some are nouns (marketplace, toolkit_info), and one uses Portuguese snake_case (restituicao_irpf_consultar). The mixed styles are readable but inconsistent.
With 7 tools, the count is reasonable and falls within the ideal range. However, most tools are generic platform utilities that could arguably be separated from the domain-specific IRPF service, but the total is still well-scoped.
The core domain operation—consulting an IRPF refund status—is fully covered by restituicao_irpf_consultar. Minor gaps exist (e.g., no batch query or history retrieval), but the essential use case is complete. The platform tools add ecosystem coverage.