Returns the current toolkit state: installed MCPs, their connection status, the accounts connected to each one, and how many catalog tools each exposes.
| 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 the tool as readOnly and idempotent. The description adds valuable context about exactly what state is returned (installed MCPs, connection status, accounts, catalog counts), which goes beyond the structured fields. No contradictions found.
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, well-structured sentence that front-loads the purpose ('Returns the current toolkit state') and then lists the components. No filler words or repetition; every word adds meaning.
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?
With no output schema, the description must convey what is returned, and it does effectively by listing the categories of information. It is sufficient for a stateless query, though it omits details on error scenarios or response format, which are not critical for such a simple 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?
The tool has zero parameters, so schema coverage is 100% (trivially). The baseline for zero parameters is 4; the description adds no parameter information since none exist, which is appropriate. It doesn't need to explain parameters that don't exist.
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 verb 'Returns' and the specific resource 'current toolkit state', enumerating the exact data points (installed MCPs, connection status, accounts, catalog tool counts). It distinguishes from siblings by focusing on overall toolkit inspection rather than authentication or specific operations.
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 a diagnostic use (inspecting state) but does not explicitly state when to use it over alternatives or mention exclusions (e.g., 'use this before other tools to check readiness'). Sibling tools like 'connect' or 'marketplace' are distinct, but no guidance is offered for when this is the right choice.
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 DETRAN query tool is clear, but `marketplace` is a meta-tool that overlaps with `connect`, `authenticate`, `toolkit_info`, and even `report_bug`. Since multiple tools can handle connection URLs, listing callable tools, reporting bugs, or running the same DETRAN query, an agent has ambiguous entry points for the same capability.
Names are mixed across styles: single tokens (`authenticate`, `connect`, `marketplace`), verb_noun (`report_bug`, `show_version`), and `detran_ms_multas_consultar`, which places the verb at the end. The set has no consistent naming convention, making the handful of tools feel more arbitrary than organized.
Seven tools is a reasonable raw number, but the the set is not well scoped: six are generic platform utilities and only one tool is actually DETRAN-specific. For a server named 'DETRAN MS: Multas', the real DETRAN surface is thin and the platform 'platform furniture' carries most of the tool count.
For the read-only consultation scope, `detran_ms_multas_consultar` covers the core operation, and the platform tools cover auth, connectivity, versioning, bug reporting, and marketplace access. Extra DETRAN workflows such as payment or appeals are not represented, but they are also not promised by the tool descriptions.