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nescoffee-create

SDMX MCP Gateway

validate_query

Validate SDMX query parameters against the 2.1 REST API specification and verify dimension codes before building the final URL.

Instructions

Validate SDMX query parameters before building the final URL.

Checks syntax according to SDMX 2.1 REST API specification.
Validates that dimension codes actually exist in the dataflow.

Args:
    dataflow_id: The dataflow to validate against
    key: The data key (dimensions separated by dots)
    filters: Dictionary of dimension_id -> code (alternative to key)
    start_period: Start of time range
    end_period: End of time range
    agency_id: The agency
    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:
    Validation results including any errors, warnings, and validated parameters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNo
filtersNo
endpointNo
agency_idNo
end_periodNo
dataflow_idYes
start_periodNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesKey that was validated
validYesWhether the query is valid
errorsNoValidation errors
warningsNoValidation warnings
suggestionNoSuggestion for fixing issues
dataflow_idYesDataflow being validated against
invalid_codesNoInvalid dimension codes if code validation was performed
Behavior3/5

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

With no annotations, the description carries full burden. It states the two validation checks and the return value (errors, warnings, validated parameters), which is useful. However, it does not disclose whether the tool makes network calls to fetch dataflow structure or what side effects occur, a notable gap given it validates against a dataflow.

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 compact and front-loaded with the primary purpose. The Args/Returns structure organizes information efficiently, and every sentence adds value without redundancy.

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 covers purpose, all parameters, and summarizes the return type, which suffices given the output schema exists. The main missing element is side-effect disclosure (e.g., network access), preventing a perfect score.

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?

Despite 0% schema description coverage, the Args section explains all seven parameters, including nuanced details like `key` being dot-separated dimensions, `filters` being a dimension-to-code dictionary, and `endpoint` targeting a provider with a default. Some entries are terse (e.g., 'agency_id: The agency') but overall the description compensates well.

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 identifies the tool as a validator of SDMX query parameters, specifying that it checks syntax per the SDMX 2.1 spec and verifies dimension code existence. This distinguishes it from sibling tools like build_data_url or probe_data_url, positioning it as a pre-build validation step.

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 phrase 'before building the final URL' provides clear contextual timing, implying use prior to URL construction. However, it does not explicitly mention when not to use it or compare it to alternatives like probe_data_url, so it falls short of a 5.

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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