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nescoffee-create

SDMX MCP Gateway

check_time_availability

Check if a time period likely has data in an SDMX dataflow, using content constraints to rule out empty periods before querying.

Instructions

Check whether a specific time period is likely to have data in a dataflow.

Uses the Actual ContentConstraint (FREQ values + TimeRange) to quickly
rule out periods that definitely have no data, without querying the data
itself. The constraint only tells us what CAN'T exist — a "plausible"
result means "worth querying", not "guaranteed to have data".

Use after identifying a dataflow and before building a data URL.
For confirmed availability, query the data directly via build_data_url().

Three-valued result:
- "no": constraint rules this out — don't bother querying
- "plausible": period within range and frequency matches — worth trying
- "plausible_different_frequency": data exists in this time window but
  at different granularity (e.g. querying monthly but only annual exists)

Args:
    dataflow_id: The dataflow to check
    query_period: The period to check (e.g. "2010", "2010-Q1", "2010-01", "2010-W05")
    agency_id: The agency (uses session endpoint if not specified)
    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:
    TimeAvailabilityResult with availability classification and reasoning

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endpointNo
agency_idNo
dataflow_idYes
query_periodYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
overlapYesTime overlap: 'full', 'partial', or 'none'
query_endYesEnd of query period (ISO date)
dataflow_idYesDataflow checked
query_startYesStart of query period (ISO date)
availabilityYes'no' (ruled out), 'plausible' (worth querying), or 'plausible_different_frequency' (data exists but at different granularity)
query_periodYesPeriod that was queried
api_calls_madeNoNumber of API calls made
interpretationYesStep-by-step reasoning
recommendationYesSuggested next action
discovery_levelNotime_availability
implied_frequencyYesImplied frequency: A, S, Q, M, W, or D
constraint_time_endNoLatest date in constraint TimeRange
available_frequenciesYesFREQ codes from the constraint
constraint_time_startNoEarliest date in constraint TimeRange
Behavior5/5

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

With no annotations provided, the description carries the full burden. It discloses how the tool works internally (Actual ContentConstraint), its limitation ('The constraint only tells us what CAN'T exist'), and exactly what 'plausible' means ('worth querying', not guaranteed). This is excellent behavioral transparency.

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

Conciseness5/5

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

The description is organized with a lead summary, then methodology, usage, result semantics, and args. Each section is information-dense and the content is front-loaded. No sentence is wasted; the structure makes it easy to scan.

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

Completeness5/5

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

The description covers the tool's purpose, usage context, result values, parameters, and even notes the return type. Even though an output schema exists, the added context about the three-valued result and its implications makes this complete for an agent to select and invoke the tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It does so thoroughly: the Args section explains every parameter, gives concrete query_period examples ('2010', '2010-Q1', '2010-01'), and clarifies the endpoint/agency_id defaults. This adds significant meaning beyond the bare 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 opens with a specific verb+resource: 'Check whether a specific time period is likely to have data in a dataflow.' It clearly distinguishes this tool from siblings by explaining that it uses the Actual ContentConstraint to rule out impossible periods, and contrasts it with build_data_url() for confirmed availability.

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

Usage Guidelines5/5

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

The description explicitly states when to use it: 'Use after identifying a dataflow and before building a data URL.' It also tells the agent when not to rely on it ('For confirmed availability, query the data directly via build_data_url()'), and explains the meaning of each result value so the agent knows how to act on the output.

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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