service_statusList all EU member states and whether each national VAT-checking service is currently Available or Unavailable, plus overall VIES (VoW) availability. Use this before/after a failed check_vat to tell a temporary national outage apart from a genuinely invalid VAT number.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations covering readOnly, idempotent, and non-destructive hints, the description adds context about what the output contains (national statuses and overall VIES availability) and the purpose behind the tool. This adds value 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 that front-load the core function and then provide usage context. Every word earns its place; no unnecessary elaboration.
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 no-parameter tool, the description fully explains what is listed and why it is useful, including the relationship to check_vat. The annotations handle safety, and the output is clearly defined in the description.
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 baseline is 4 per the rubric. The description doesn't need to explain parameters, and the empty schema already covers the 100% parameter definition.
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 uses the verb 'List' and specifies the exact resource: EU member states' VAT-checking service availability plus overall VIES status. It distinguishes itself from the sibling check_vat by stating it's for distinguishing outages from invalid VAT numbers.
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 explicitly identifies when to use this tool: before/after a failed check_vat to determine if an outage is causing the failure. This is clear usage context, though it doesn't explicitly state when NOT to use it or mention alternative tools beyond check_vat.
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.
Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical to the stable router right now), ask_pipeworx_grounded, and deep_research all route the same class of questions, making mis-selection easy. The six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also blur together around edge detection and arbitrage, further muddying tool boundaries.
All 33 tools use consistent lowercase snake_case naming, and most follow a clear verb_noun pattern (check_vat, compare_entities, resolve_entity, unsubscribe). A handful of noun-style names (entity_profile, bet_research, polymarket_edges, recent_alerts) deviate from the verb-first pattern but are still predictable and readable.
33 tools is beyond the 25+ threshold for a coherent set, and the scope is sprawling: universal data routing, prediction-market analytics, VAT validation, AI visibility, memory, subscriptions, npm dependency scanning, and feedback. While each sub-domain has reason to exist, bundling them all into one server creates a kitchen-sink feel with too many entry points.
The core data-routing domain is well covered: universal router, grounded mode, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and search-within. VAT has check + status, and memory/subscription lifecycles are complete. However, there is no standalone tool to fetch a raw pipeworx:// record that citations reference, and the extreme breadth means no single domain is exhaustively fleshed out.