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nescoffee-create

SDMX MCP Gateway

get_data_availability

Check if data exists for a dataflow or specific dimension combinations before building data URLs, preventing empty query results.

Instructions

Get actual data availability for a dataflow or specific dimension combinations.

This tool is critical for avoiding empty query results. Use it to check
if data exists before building the final data URL.

Args:
    dataflow_id: The dataflow to check
    filters: Optional dict of dimension=value pairs to check
    agency_id: The agency ID
    endpoint: Optional endpoint key (e.g. "FBOS", "ECB") to target a
        specific provider for this call only. Defaults to the session's
        current endpoint.

Returns:
    Information about what data exists, including time ranges and suggestions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNo
endpointNo
agency_idNo
dataflow_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoWhy the answer is empty, when it is
time_rangeNoAvailable time period range
data_existsNoWhether data exists for checked combination
dataflow_idYesDataflow identifier
cube_regionsNoSpecific data regions available
constraint_idNoConstraint identifier if available
has_constraintYesWhether availability constraints exist
interpretationNoHuman-readable interpretation
recommendationNoRecommendation based on availability
constraint_typeNoActual (confirmed data) or Allowed (schema-permitted)
discovery_levelNoDiscovery workflow levelavailability
observation_countNoObservation count if the provider exposes it
dimension_values_checkedNoDimension values that were checked
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the return value ('information about what data exists, including time ranges and suggestions'), explains the endpoint override behavior, and implies a read-only operation through the 'get' verb. Missing details like permissions or side effects, but these are less critical for a read-only check tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately structured with a brief context paragraph, an Args section, and a Returns line. Every sentence serves a purpose, though the Args section could be slightly more compact. Overall, it is well-organized and not bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides purpose, usage, parameter semantics, and return information. An output schema exists, so detailed return formatting is not necessary. The mention of avoiding empty query results and targeting a provider adds valuable context. Minor gaps include lack of error handling or example usage, but these are not essential for tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no descriptions (0% coverage), so the description must compensate. It explains all four parameters: dataflow_id (the dataflow to check), filters (optional dimension=value pairs), agency_id (agency ID), and endpoint (optional provider override with default). This adds significant meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (Get) and resource (data availability) and specifies the scope (dataflow or specific dimension combinations). It distinguishes this tool from siblings like check_time_availability and validate_query by focusing on actual data availability.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance on when to use the tool: 'critical for avoiding empty query results' and 'check if data exists before building the final data URL.' It does not mention alternatives or exclusions, but the usage context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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