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imf-mcp-server

by io.github.cyanheads

Imf List Databases

imf_list_databases
Read-onlyIdempotent

List IMF SDMX dataflows available on the portal. Entry point for every query: imf_get_database and imf_query_dataset both require a dataflow id obtained here. Vintage (historical snapshot) dataflows such as WEO_2025_OCT_VINTAGE are excluded by default; set include_vintages=true to include them. Results are paged — 50 per call by default, adjustable with limit and offset — and total_count reports how many dataflows matched. Descriptions are shortened here; imf_get_database returns the full text for a single dataflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum dataflows to return in this call. Default 50, ceiling 200; total_count reports how many matched, so a partial page is always recognizable as one.
filterNoOptional name, ID, or description substring to filter results. Case-insensitive. Example: "exchange rate" returns ER and related dataflows.
offsetNoNumber of matching dataflows to skip before this page. Combine with limit to page through a broad or unfiltered catalog.
include_vintagesNoInclude vintage (historical snapshot) dataflows such as WEO_2025_OCT_VINTAGE. Default false — vintages are excluded to keep the discovery surface clean.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
capNoThe limit that bounded this page.
errorNoPresent when the call failed. Absent on success.
shownNoDataflows returned in this page.
noticeNoPopulated when the filter matches nothing, or when matches remain beyond this page — explains why and names the next offset to request.
offsetNoNumber of matching dataflows skipped before this page.
dataflowsNoThis page of matching dataflows; pass the id to imf_get_database to resolve dimension codelists.
truncatedNoTrue when matching dataflows remain beyond this page.
total_countNoDataflows matching filter and include_vintages, before limit and offset are applied. Exceeds returned_count when more pages remain.
returned_countNoDataflows in this page — the length of dataflows.

TDQS

A4.7/5.0
Behavior5/5

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

With readOnlyHint, openWorldHint, and idempotentHint already present, the description adds meaningful behavioral context beyond those annotations: vintages are excluded by default, results are paged at 50 per call, total_count reports matches, and descriptions are shortened in this listing. These traits are not derivable from the annotations and materially shape how an agent interprets results.

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 a compact four-sentence paragraph, each sentence earning its place: core action, entry-point role, the vintage caveat, pagination behavior, and a pointer to an alternative for fuller descriptions. It is front-loaded with the primary purpose and contains no filler or redundant restatements.

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 essential operational context: why this tool must be called first, default exclusions, pagination semantics, total_count availability, and where to get fuller data. Since an output schema exists, the description need not spell out the return shape, and nothing required for correct invocation is missing.

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

Parameters3/5

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

The input schema already covers all four parameters with defaults and prose descriptions at 100% coverage, so the baseline applies. The description's paging and vintage-exclusion statements mirror schema content rather than adding new semantic information, though they do reinforce the intended use of limit, offset, and include_vintages in context.

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 and resource: 'List IMF SDMX dataflows available on the portal.' It clearly distinguishes the tool from siblings by identifying imf_get_database and imf_query_dataset as consumers of the IDs returned here, and contrasts the abbreviated descriptions with the full text available from imf_get_database.

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 positions the tool as the 'Entry point for every query' and notes that both imf_get_database and imf_query_dataset require a dataflow id obtained here, telling agents when to use it. It also provides guidance on alternatives by noting that full descriptions live in imf_get_database, and sprinkles conditionals like 'set include_vintages=true' for when vintages are needed.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct role: discovering dataflows, inspecting dimensions/codelists, querying SDMX data, and analyzing staged DataCanvas tables. The two query-like tools are separated by their data source (live SDMX vs. staged results), and the descriptions reinforce the required sequencing.

Naming Consistency4/5

The imf_ prefix and snake_case convention are consistent, and most tools follow verb_noun naming (list_databases, get_database, query_dataset). However, imf_dataframe_describe and imf_dataframe_query place the object before the verb, deviating slightly from the otherwise predictable pattern.

Tool Count5/5

Five tools is well-scoped for a read-only IMF data access server. Each tool covers a necessary stage in the workflow without redundancy or bloat.

Completeness5/5

The tool set covers the full read-only lifecycle: discover dataflows, inspect required dimension codes, query series, and analyze large result sets via SQL. No obvious gaps exist for the stated purpose, and write operations are not relevant to this domain.

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