get_viewMetadata for one Statbel view by its id (a UUID, e.g. from list_views). Returns the view object: name, locale, dataSourceId (the underlying dataset), and publish/change dates. To get the dataset description in all available languages, pass the returned dataSourceId to get_dataset.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | View UUID, e.g. "1be9b77f-4005-4d58-a885-8281b5bbe617". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": "1be9b77f-4005-4d58-a885-8281b5bbe617"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent behavior. The description adds value by listing the returned fields (name, locale, dataSourceId, dates) and the relationship to get_dataset via dataSourceId. No contradictions with 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?
Two sentences: first states purpose and input source, second explains return fields and next step. Every sentence carries useful information with no fluff, well front-loaded.
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 one-parameter tool with no output schema, the description fully covers what the tool returns and how to use the result. It is complete enough for an agent to select and invoke it correctly.
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?
Schema covers the id parameter fully (100% coverage). The description enhances it by noting the id is a UUID typically obtained from list_views, which is not in the schema's description, adding operational context.
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 it retrieves metadata for one Statbel view by id, using a specific verb/resource. It distinguishes itself from siblings like list_views (which lists views) and get_dataset (which gets dataset descriptions) by specifying it returns view metadata.
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 explains the id comes from list_views, implying the tool is for fetching a single view's metadata after listing. It also directs users to get_dataset for language-specific dataset descriptions, providing a clear follow-up. Lacks an explicit 'when not to use' but context is sufficient.
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 set contains several clusters of overlapping tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve broad data-query purposes, while the six Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap in scope. The detailed descriptions help, but an agent would frequently struggle to choose the correct tool among near-synonyms.
All names use snake_case, but the underlying pattern is inconsistent: list_*/get_* for Statbel, ask_* for query routers, noun-heavy names like entity_profile, bet_research, polymarket_edges, and recent_alerts, plus bare verbs like remember, recall, forget, subscribe, unsubscribe. It remains readable, but there is no single predictable verb_noun convention across the set.
At 35 tools, the set exceeds the range where each tool clearly earns its place, and the scope is wildly broad: Belgian statistics, general data lookup, prediction markets, npm dependency checks, memory, subscriptions, llms.txt generation, and AI visibility. A server named 'Statbel Be' carries 31 tools unrelated to that name, which makes the count feel bloated and unfocused.
For the Statbel domain implied by the server name, the surface is severely incomplete: list_datasets, get_dataset, list_views, and get_view only return metadata — there is no tool to actually fetch the statistical data values. For the broader Pipeworx data-access domain, coverage is more complete, but the server's stated purpose is under-served and leaves core workflows at a dead end.