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Sdmx Find Dataflow

sdmx_find_dataflow
Read-onlyIdempotent

Keyword-search a registry's full dataflow catalogue by id or name substring. Use this to find the dataflow id to pass to sdmx_dataflow_structure when you only know a topic. Example: sdmx_find_dataflow({ registry: "bis", query: "credit" }) or sdmx_find_dataflow({ registry: "oecd", query: "unemployment" }). For Eurostat specifically, the eurostat pack's own search_datasets tool searches its ~10,000-entry table of contents with richer metadata (last-update dates, time coverage) and is usually the better choice.

Input Schema

NameRequiredDescriptionDefault
limitNoMax results to return (default 50, max 300).
queryYesCase-insensitive substring to match against dataflow id or name, e.g. "unemployment", "credit", "exchange rate".
agencyNoRestrict to one maintaining agency id instead of the registry default, e.g. "OECD.SDD.NAD".
registryYesWhich SDMX registry to query: eurostat, oecd, ecb, unsd, imf, bis, ilo, abs.

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/idempotent, so the safety profile is covered. The description adds real context beyond that: it searches the full catalogue rather than a curated subset, and it discloses the Eurostat corpus size (~10,000 entries) that motivates the alternative. It does not describe result shape or pagination behavior, keeping it short of a 5.

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?

Three sentences, front-loaded with the core capability, then examples, then the exception case. Every sentence carries distinct information; the examples are concrete rather than filler.

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?

For a read-only search tool with full schema coverage and no output schema, the description covers purpose, selection criteria, and the key exception. The one gap is that it doesn't say what a result looks like (ids plus names) or that results are truncated by limit, which matters for a search tool.

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?

Schema coverage is 100%, so a 3 would be the floor, but the description earns above it by giving concrete worked calls (registry "bis" with query "credit", registry "oecd" with query "unemployment") that show how registry and query interact in practice. It adds no new meaning to limit or agency beyond the 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?

States a specific verb ('Keyword-search') and resource ('registry's full dataflow catalogue by id or name substring'), and explicitly frames its output as a dataflow id. The sibling relationship to sdmx_dataflow_structure is stated, so an agent can distinguish it without opening a schema.

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?

Gives an explicit when-to-use ('when you only know a topic') and an explicit when-to-use-something-else: for Eurostat, the eurostat pack's search_datasets is 'usually the better choice,' with the reason (richer metadata, last-update, time coverage). Nothing is left to inference.

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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