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Imf Query Dataset

imf_query_dataset
Read-onlyIdempotent

Query an IMF SDMX dataflow by dimension key over a time range. Returns observations with time_period, value, and status, plus the unit, scale, and decimals of each series — a key resolving to several series carries one entry per series in series_metadata, since unit and scale differ between them. Requires imf_get_database first to obtain the correct key_format and valid dimension codes. Country codes are ISO 3-letter (USA, GBR, DEU — not US, GB, DE). Key format: dot-separated codes in DSD keyPosition order (e.g. USA.NGDP_RPCH.A for WEO). Every position must carry a code: use + to combine codes (e.g. USA+GBR.NGDP_RPCH.A) and * to match every code at a position (e.g. *.NGDP_RPCH.A for all countries). Codelists from imf_get_database enumerate the code universe, not actual coverage — valid codes can still return no_data if the combination has no series. start_period and end_period must be valid period strings (YYYY, YYYY-SN, YYYY-QN, YYYY-MM, or a calendar-valid YYYY-MM-DD) with start_period no later than end_period; malformed or reversed ranges are rejected. A bound covers the whole period it names, so end_period 2023 includes 2023-M12 and 2023-Q4. Large analytical result sets (multi-country, long time range) spill to DataCanvas; call imf_dataframe_describe first to inspect staged tables and columns, then imf_dataframe_query for SQL analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesDot-separated dimension codes in DSD keyPosition order. Call imf_get_database to get key_format and valid codes first. Use + to combine codes at one position (e.g. USA+GBR.NGDP_RPCH.A). Use * to match every code at a position — *.NGDP_RPCH.A returns the indicator for all countries, and CAN.*.A every indicator for Canada. Every position needs a code or a *; an empty segment (USA..A) is rejected. Country codes are ISO 3-letter: USA not US, GBR not GB, DEU not DE.
versionNoDataflow version. Auto-detected from dataflow list when omitted.
agency_idNoAgency ID, e.g. IMF.RES or IMF.STA. Auto-detected from dataflow list when omitted.
canvas_idNoExisting canvas ID to accumulate results into across multiple queries. This selects the destination only; it does not force staging. Use output_mode="canvas" to stage an under-budget result.
end_periodNoEnd of time range (inclusive). Same formats as start_period, and must not be earlier than it. The bound covers the whole period it names, so end_period 2023 admits 2023-M12 and 2023-Q4. Observations after this period are excluded from the result.
dataflow_idYesDataflow identifier from imf_list_databases, e.g. WEO, BOP, CPI.
output_modeNoResult placement. auto returns an under-budget result inline and spills only when needed. canvas explicitly stages the full result, using canvas_id when supplied or allocating a fresh canvas.auto
start_periodNoStart of time range (inclusive). Accepts any of YYYY (annual), YYYY-SN (semi-annual, e.g. 2023-S1), YYYY-QN (quarterly, e.g. 2023-Q1), YYYY-MM (monthly), or a calendar-valid YYYY-MM-DD (daily), whatever the dataflow's frequency. The bound covers the whole period it names, so start_period 2023 admits 2023-M01 and 2023-Q1. Observations before this period are excluded from the result.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoDimension key used in the query, e.g. USA.NGDP_RPCH.A.
errorNoPresent when the call failed. Absent on success.
noticeNoPopulated when a period bound was set but some observations carry a time_period label the range filter does not recognize. Composes with staged retrieval_guidance when both apply.
sourceNoAttribution string required by IMF data terms: "Source: International Monetary Fund, <dataflow name>, <link>".
stagedNoTrue when the complete observation set is stored on DataCanvas. canvas_id and table_name are present whenever true.
canvas_idNoDataCanvas session ID — present when staged=true. Pass first to imf_dataframe_describe, then to imf_dataframe_query.
truncatedNoTrue only when observations is an incomplete preview of observation_count. A result can be staged=true and truncated=false when every observation also fits inline.
end_periodNoLatest period covered; absent when the full available range was used.
table_nameNoDuckDB table name on the canvas — present when staged=true; reference in SQL via FROM <table_name>.
dataflow_idNoDataflow identifier that was queried, e.g. WEO.
observationsNoInline observation preview. For staged results this may contain the full set or a budget-limited prefix; observation_count remains the full count.
start_periodNoEarliest period covered; absent when the full available range was used.
series_metadataNoPer-series attributes, one entry per distinct series_key in the result. Present only when the query resolved to more than one series; a single-series query carries its values in series_attributes instead. Unit and scale differ across series in one query — WEO NGDPD is USD at scale 9 while NGDP_RPCH is PT unscaled — so interpret each series against its own entry.
observation_countNoTotal observations in the result.
series_attributesNoAttributes of the first series in the result — the same series as series_metadata[0]. A key with + or * resolves to several series whose scale and unit differ, and this field describes only the first of them: read series_metadata for the rest, and never apply these values to another series_key.
retrieval_guidanceNoPresent on every staged result. Identifies the imf_dataframe_describe-before-imf_dataframe_query retrieval workflow.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed18 schema fields changed
    • addedInput schema / properties / canvas_id / pattern
      Added value: +"^[A-Za-z0-9_-]{10}$"
    • changedOutput schema / properties / error / properties / data / properties / reason / description
      Previous value: -"Machine-readable failure mode. Declared by this tool: `dataflow_not_found`: dataflow_id does not match any known dataflow on api.imf.org `no_data`: Key is structurally valid but the dataflow holds no series for this code combination, or the dataflow publishes no series at all `no_data_in_range`: The key returned observations but start_period/end_period excluded every one of them `key_dimension_mismatch`: Number of dot-separated segments in key does not match the dataflow's DSD dimension count `empty_key_segment`: A dot-separated position in key is empty or blank, which matches no series upstream `invalid_period_format`: start_period or end_period is not one of the recognized period formats `invalid_period_range`: start_period is later than end_period `structure_unavailable`: The dataflow structure (DSD) cannot be fetched after the dataflow catalog resolved successfully `canvas_unavailable`: output_mode=\"canvas\" was requested but DataCanvas is disabled `response_too_large`: Fixed staged-result metadata exceeds the response budget before any observation preview can be included `dataflow_list_unavailable`: The dataflow catalog that dataflow_id is resolved against could not be fetched — fires before the DSD and data lookups are attempted Other values are possible when a failure originates below the handler."New value: +"Machine-readable failure mode. Declared by this tool: `dataflow_not_found`: dataflow_id does not match any known dataflow on api.imf.org. `no_data`: Key is structurally valid but the dataflow holds no series for this code combination, or the dataflow publishes no series at all. `no_data_in_range`: The key returned observations but start_period/end_period excluded every one of them. `key_dimension_mismatch`: Number of dot-separated segments in key does not match the dataflow's DSD dimension count. `empty_key_segment`: A dot-separated position in key is empty or blank, which matches no series upstream. `invalid_period_format`: start_period or end_period is not one of the recognized period formats. `invalid_period_range`: start_period is later than end_period. `structure_unavailable`: The dataflow structure (DSD) cannot be fetched after the dataflow catalog resolved successfully. `canvas_unavailable`: output_mode=\"canvas\" was requested but DataCanvas is disabled. `response_too_large`: Fixed staged-result metadata exceeds the response budget before any observation preview can be included. `dataflow_list_unavailable`: The dataflow catalog that dataflow_id is resolved against could not be fetched — fires before the DSD and data lookups are attempted. Other values are possible when a failure originates below the handler."
    • removedOutput schema / properties / observations / items / properties / status / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / observations / items / properties / status / type
      Added value: +[
      +  "string",
      +  "null"
      +]
    • removedOutput schema / properties / observations / items / properties / value / anyOf
      Removed value: -[
      -  {
      -    "type": "number"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / observations / items / properties / value / type
      Added value: +[
      +  "number",
      +  "null"
      +]
    • removedOutput schema / properties / series_attributes / properties / decimals / anyOf
      Removed value: -[
      -  {
      -    "type": "number"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / series_attributes / properties / decimals / type
      Added value: +[
      +  "number",
      +  "null"
      +]
    • removedOutput schema / properties / series_attributes / properties / scale / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / series_attributes / properties / scale / type
      Added value: +[
      +  "string",
      +  "null"
      +]
    • removedOutput schema / properties / series_attributes / properties / unit / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / series_attributes / properties / unit / type
      Added value: +[
      +  "string",
      +  "null"
      +]
    • removedOutput schema / properties / series_metadata / items / properties / decimals / anyOf
      Removed value: -[
      -  {
      -    "type": "number"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / series_metadata / items / properties / decimals / type
      Added value: +[
      +  "number",
      +  "null"
      +]
    • removedOutput schema / properties / series_metadata / items / properties / scale / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / series_metadata / items / properties / scale / type
      Added value: +[
      +  "string",
      +  "null"
      +]
    • removedOutput schema / properties / series_metadata / items / properties / unit / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedOutput schema / properties / series_metadata / items / properties / unit / type
      Added value: +[
      +  "string",
      +  "null"
      +]
  2. Changed13 schema fields changed
    • changedInput schema / properties / canvas_id / description
      Previous value: -"Existing canvas ID to accumulate results into across multiple queries. Omit to allocate a fresh canvas; the response includes a canvas_id when results spill to DataCanvas."New value: +"Existing canvas ID to accumulate results into across multiple queries. This selects the destination only; it does not force staging. Use output_mode=\"canvas\" to stage an under-budget result."
    • addedInput schema / properties / output_mode
      Added value: +{
      +  "default": "auto",
      +  "description": "Result placement. auto returns an under-budget result inline and spills only when needed. canvas explicitly stages the full result, using canvas_id when supplied or allocating a fresh canvas.",
      +  "enum": [
      +    "auto",
      +    "canvas"
      +  ],
      +  "type": "string"
      +}
    • changedInput schema / properties / start_period / description
      Previous value: -"Start of time range (inclusive). Accepts any of YYYY (annual), YYYY-SN (semi-annual, e.g. 2023-S1), YYYY-QN (quarterly, e.g. 2023-Q1), YYYY-MM (monthly), or YYYY-MM-DD (daily), whatever the dataflow's frequency. The bound covers the whole period it names, so start_period 2023 admits 2023-M01 and 2023-Q1. Observations before this period are excluded from the result."New value: +"Start of time range (inclusive). Accepts any of YYYY (annual), YYYY-SN (semi-annual, e.g. 2023-S1), YYYY-QN (quarterly, e.g. 2023-Q1), YYYY-MM (monthly), or a calendar-valid YYYY-MM-DD (daily), whatever the dataflow's frequency. The bound covers the whole period it names, so start_period 2023 admits 2023-M01 and 2023-Q1. Observations before this period are excluded from the result."
    • changedOutput schema / anyOf
      Previous value: -[
      -  {
      -    "not": {
      -      "required": [
      -        "error"
      -      ]
      -    },
      -    "required": [
      -      "dataflow_id",
      -      "key",
      -      "observations",
      -      "series_attributes",
      -      "observation_count",
      -      "truncated",
      -      "source"
      -    ]
      -  },
      -  {
      -    "required": [
      -      "error"
      -    ]
      -  }
      -]New value: +[
      +  {
      +    "not": {
      +      "required": [
      +        "error"
      +      ]
      +    },
      +    "required": [
      +      "dataflow_id",
      +      "key",
      +      "observations",
      +      "series_attributes",
      +      "observation_count",
      +      "staged",
      +      "truncated",
      +      "source"
      +    ]
      +  },
      +  {
      +    "required": [
      +      "error"
      +    ]
      +  }
      +]
    • changedOutput schema / properties / canvas_id / description
      Previous value: -"DataCanvas session ID — present when truncated=true. Pass to imf_dataframe_query or imf_dataframe_describe to query the full result."New value: +"DataCanvas session ID — present when staged=true. Pass first to imf_dataframe_describe, then to imf_dataframe_query."
    • changedOutput schema / properties / error / properties / data / properties / reason / description
      Previous value: -"Machine-readable failure mode. Declared by this tool: `dataflow_not_found`: dataflow_id does not match any known dataflow on api.imf.org `no_data`: Key is structurally valid but the dataflow holds no series for this code combination, or the dataflow publishes no series at all `no_data_in_range`: The key returned observations but start_period/end_period excluded every one of them `key_dimension_mismatch`: Number of dot-separated segments in key does not match the dataflow's DSD dimension count `empty_key_segment`: A dot-separated position in key is empty or blank, which matches no series upstream `invalid_period_format`: start_period or end_period is not one of the recognized period formats `invalid_period_range`: start_period is later than end_period `structure_unavailable`: api.imf.org returns non-200 on the data endpoint `dataflow_list_unavailable`: The dataflow catalog that dataflow_id is resolved against could not be fetched — fires before the DSD and data lookups are attempted Other values are possible when a failure originates below the handler."New value: +"Machine-readable failure mode. Declared by this tool: `dataflow_not_found`: dataflow_id does not match any known dataflow on api.imf.org `no_data`: Key is structurally valid but the dataflow holds no series for this code combination, or the dataflow publishes no series at all `no_data_in_range`: The key returned observations but start_period/end_period excluded every one of them `key_dimension_mismatch`: Number of dot-separated segments in key does not match the dataflow's DSD dimension count `empty_key_segment`: A dot-separated position in key is empty or blank, which matches no series upstream `invalid_period_format`: start_period or end_period is not one of the recognized period formats `invalid_period_range`: start_period is later than end_period `structure_unavailable`: The dataflow structure (DSD) cannot be fetched after the dataflow catalog resolved successfully `canvas_unavailable`: output_mode=\"canvas\" was requested but DataCanvas is disabled `response_too_large`: Fixed staged-result metadata exceeds the response budget before any observation preview can be included `dataflow_list_unavailable`: The dataflow catalog that dataflow_id is resolved against could not be fetched — fires before the DSD and data lookups are attempted Other values are possible when a failure originates below the handler."
    • changedOutput schema / properties / error / properties / data / properties / reason / examples
      Previous value: -[
      -  "dataflow_not_found",
      -  "no_data",
      -  "no_data_in_range",
      -  "key_dimension_mismatch",
      -  "empty_key_segment",
      -  "invalid_period_format",
      -  "invalid_period_range",
      -  "structure_unavailable",
      -  "dataflow_list_unavailable"
      -]New value: +[
      +  "dataflow_not_found",
      +  "no_data",
      +  "no_data_in_range",
      +  "key_dimension_mismatch",
      +  "empty_key_segment",
      +  "invalid_period_format",
      +  "invalid_period_range",
      +  "structure_unavailable",
      +  "canvas_unavailable",
      +  "response_too_large",
      +  "dataflow_list_unavailable"
      +]
    • changedOutput schema / properties / notice / description
      Previous value: -"Populated when a period bound was set but some observations carry a time_period label the range filter does not recognize — those rows are returned unfiltered, so the requested range did not apply to them."New value: +"Populated when a period bound was set but some observations carry a time_period label the range filter does not recognize. Composes with staged retrieval_guidance when both apply."
    • changedOutput schema / properties / observations / description
      Previous value: -"Inline observations. Empty when results spilled to canvas (see canvas_id / table_name)."New value: +"Inline observation preview. For staged results this may contain the full set or a budget-limited prefix; observation_count remains the full count."
    • addedOutput schema / properties / retrieval_guidance
      Added value: +{
      +  "description": "Present on every staged result. Identifies the imf_dataframe_describe-before-imf_dataframe_query retrieval workflow.",
      +  "type": "string"
      +}
    • addedOutput schema / properties / staged
      Added value: +{
      +  "description": "True when the complete observation set is stored on DataCanvas. canvas_id and table_name are present whenever true.",
      +  "type": "boolean"
      +}
    • changedOutput schema / properties / table_name / description
      Previous value: -"DuckDB table name on the canvas — present when truncated=true; reference in SQL via FROM <table_name>."New value: +"DuckDB table name on the canvas — present when staged=true; reference in SQL via FROM <table_name>."
    • changedOutput schema / properties / truncated / description
      Previous value: -"True when the result exceeded the inline limit and was staged on a DataCanvas table; canvas_id and table_name are populated and imf_dataframe_query provides SQL access to the full set."New value: +"True only when observations is an incomplete preview of observation_count. A result can be staged=true and truncated=false when every observation also fits inline."
  3. Changed6 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedInput schema / additionalProperties
      Added value: +false
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedOutput schema / anyOf
      Added value: +[
      +  {
      +    "not": {
      +      "required": [
      +        "error"
      +      ]
      +    },
      +    "required": [
      +      "dataflow_id",
      +      "key",
      +      "observations",
      +      "series_attributes",
      +      "observation_count",
      +      "truncated",
      +      "source"
      +    ]
      +  },
      +  {
      +    "required": [
      +      "error"
      +    ]
      +  }
      +]
    • addedOutput schema / properties / error
      Added value: +{
      +  "additionalProperties": {},
      +  "description": "Present when the call failed. Absent on success.",
      +  "properties": {
      +    "code": {
      +      "description": "JSON-RPC error code for this failure.",
      +      "maximum": 9007199254740991,
      +      "minimum": -9007199254740991,
      +      "type": "integer"
      +    },
      +    "data": {
      +      "additionalProperties": {},
      +      "properties": {
      +        "reason": {
      +          "description": "Machine-readable failure mode. Declared by this tool: `dataflow_not_found`: dataflow_id does not match any known dataflow on api.imf.org `no_data`: Key is structurally valid but the dataflow holds no series for this code combination, or the dataflow publishes no series at all `no_data_in_range`: The key returned observations but start_period/end_period excluded every one of them `key_dimension_mismatch`: Number of dot-separated segments in key does not match the dataflow's DSD dimension count `empty_key_segment`: A dot-separated position in key is empty or blank, which matches no series upstream `invalid_period_format`: start_period or end_period is not one of the recognized period formats `invalid_period_range`: start_period is later than end_period `structure_unavailable`: api.imf.org returns non-200 on the data endpoint `dataflow_list_unavailable`: The dataflow catalog that dataflow_id is resolved against could not be fetched — fires before the DSD and data lookups are attempted Other values are possible when a failure originates below the handler.",
      +          "examples": [
      +            "dataflow_not_found",
      +            "no_data",
      +            "no_data_in_range",
      +            "key_dimension_mismatch",
      +            "empty_key_segment",
      +            "invalid_period_format",
      +            "invalid_period_range",
      +            "structure_unavailable",
      +            "dataflow_list_unavailable"
      +          ],
      +          "type": "string"
      +        },
      +        "recovery": {
      +          "additionalProperties": {},
      +          "description": "Actionable next step for the caller.",
      +          "properties": {
      +            "hint": {
      +              "type": "string"
      +            }
      +          },
      +          "required": [
      +            "hint"
      +          ],
      +          "type": "object"
      +        },
      +        "retryable": {
      +          "description": "Whether retrying may succeed.",
      +          "type": "boolean"
      +        }
      +      },
      +      "type": "object"
      +    },
      +    "message": {
      +      "description": "Human-readable description of what went wrong.",
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "code",
      +    "message"
      +  ],
      +  "type": "object"
      +}
    • removedOutput schema / required
      Removed value: -[
      -  "dataflow_id",
      -  "key",
      -  "observations",
      -  "series_attributes",
      -  "observation_count",
      -  "truncated",
      -  "source"
      -]
  4. Changed3 schema fields changed
    • changedOutput schema / properties / series_attributes / properties / unit / description
      Previous value: -"Unit of measure, e.g. Percent, USD."New value: +"Unit of measure as the upstream code, e.g. PT (percent), USD, XDC (domestic currency), NUM (count). Null when the response carries no unit for the series — many dataflows publish none."
    • changedOutput schema / properties / series_metadata / description
      Previous value: -"Per-series attributes, one entry per distinct series_key in the result. Present only when the query resolved to more than one series; a single-series query carries its values in series_attributes instead. Scale differs across series in one query — WEO NGDPD is scale 9 while NGDP_RPCH is unscaled — so interpret each series against its own entry."New value: +"Per-series attributes, one entry per distinct series_key in the result. Present only when the query resolved to more than one series; a single-series query carries its values in series_attributes instead. Unit and scale differ across series in one query — WEO NGDPD is USD at scale 9 while NGDP_RPCH is PT unscaled — so interpret each series against its own entry."
    • changedOutput schema / properties / series_metadata / items / properties / unit / description
      Previous value: -"Unit of measure for this series, e.g. Percent."New value: +"Unit of measure for this series as the upstream code, e.g. PT (percent), USD, XDC (domestic currency). Null when the response carries none for it."
  5. Changed3 schema fields changed
    • changedOutput schema / properties / series_attributes / description
      Previous value: -"Series-level attributes (unit, scale, decimals)."New value: +"Attributes of the first series in the result — the same series as series_metadata[0]. A key with + or * resolves to several series whose scale and unit differ, and this field describes only the first of them: read series_metadata for the rest, and never apply these values to another series_key."
    • changedOutput schema / properties / series_attributes / properties / scale / description
      Previous value: -"Scale multiplier, e.g. Billions."New value: +"Scale multiplier as the upstream code, e.g. 9 for billions. \"0\" means no multiplier — the values are unscaled."
    • addedOutput schema / properties / series_metadata
      Added value: +{
      +  "description": "Per-series attributes, one entry per distinct series_key in the result. Present only when the query resolved to more than one series; a single-series query carries its values in series_attributes instead. Scale differs across series in one query — WEO NGDPD is scale 9 while NGDP_RPCH is unscaled — so interpret each series against its own entry.",
      +  "items": {
      +    "additionalProperties": false,
      +    "description": "Unit, scale, and decimals for one series in the result.",
      +    "properties": {
      +      "decimals": {
      +        "anyOf": [
      +          {
      +            "type": "number"
      +          },
      +          {
      +            "type": "null"
      +          }
      +        ],
      +        "description": "Number of decimal places shown for this series."
      +      },
      +      "scale": {
      +        "anyOf": [
      +          {
      +            "type": "string"
      +          },
      +          {
      +            "type": "null"
      +          }
      +        ],
      +        "description": "Scale multiplier for this series as the upstream code, e.g. 9 for billions. \"0\" means no multiplier."
      +      },
      +      "series_key": {
      +        "description": "Series these attributes belong to, matching observations[].series_key.",
      +        "type": "string"
      +      },
      +      "unit": {
      +        "anyOf": [
      +          {
      +            "type": "string"
      +          },
      +          {
      +            "type": "null"
      +          }
      +        ],
      +        "description": "Unit of measure for this series, e.g. Percent."
      +      }
      +    },
      +    "required": [
      +      "series_key",
      +      "unit",
      +      "scale",
      +      "decimals"
      +    ],
      +    "type": "object"
      +  },
      +  "type": "array"
      +}
  6. Changed5 schema fields changed
    • changedInput schema / properties / end_period / description
      Previous value: -"End of time range (inclusive). Same format as start_period, and must be greater than or equal to start_period. Observations after this period are excluded from the result."New value: +"End of time range (inclusive). Same formats as start_period, and must not be earlier than it. The bound covers the whole period it names, so end_period 2023 admits 2023-M12 and 2023-Q4. Observations after this period are excluded from the result."
    • changedInput schema / properties / key / description
      Previous value: -"Dot-separated dimension codes in DSD keyPosition order. Call imf_get_database to get key_format and valid codes first. Use + to specify multiple codes (e.g. USA+GBR.NGDP_RPCH.A). Country codes are ISO 3-letter: USA not US, GBR not GB, DEU not DE."New value: +"Dot-separated dimension codes in DSD keyPosition order. Call imf_get_database to get key_format and valid codes first. Use + to combine codes at one position (e.g. USA+GBR.NGDP_RPCH.A). Use * to match every code at a position — *.NGDP_RPCH.A returns the indicator for all countries, and CAN.*.A every indicator for Canada. Every position needs a code or a *; an empty segment (USA..A) is rejected. Country codes are ISO 3-letter: USA not US, GBR not GB, DEU not DE."
    • changedInput schema / properties / start_period / description
      Previous value: -"Start of time range (inclusive). Format matches the dataflow frequency: YYYY (annual), YYYY-QN (quarterly, e.g. 2023-Q1), YYYY-MM (monthly). Observations before this period are excluded from the result."New value: +"Start of time range (inclusive). Accepts any of YYYY (annual), YYYY-SN (semi-annual, e.g. 2023-S1), YYYY-QN (quarterly, e.g. 2023-Q1), YYYY-MM (monthly), or YYYY-MM-DD (daily), whatever the dataflow's frequency. The bound covers the whole period it names, so start_period 2023 admits 2023-M01 and 2023-Q1. Observations before this period are excluded from the result."
    • addedOutput schema / properties / notice
      Added value: +{
      +  "description": "Populated when a period bound was set but some observations carry a time_period label the range filter does not recognize — those rows are returned unfiltered, so the requested range did not apply to them.",
      +  "type": "string"
      +}
    • changedOutput schema / properties / observations / items / properties / time_period / description
      Previous value: -"Time label as emitted by the upstream API. Annual: YYYY (e.g. 2023). Quarterly: YYYY-QN (e.g. 2023-Q1). Monthly: YYYY-MNN (e.g. 2023-M01, not YYYY-MM). start_period/end_period accept both YYYY-MM and YYYY-MNN for monthly comparisons."New value: +"Time label as emitted by the upstream API. Annual: YYYY (e.g. 2023). Semi-annual: YYYY-SN (e.g. 2023-S1). Quarterly: YYYY-QN (e.g. 2023-Q1). Monthly: YYYY-MNN (e.g. 2023-M01, not YYYY-MM). Daily: YYYY-MM-DD (e.g. 2023-01-05). Every one of these is also accepted as a start_period/end_period bound, so a label from this field can be passed straight back in."
  7. Changed1 schema field changed
    • changedInput schema / properties / end_period / description
      Previous value: -"End of time range (inclusive). Same format as start_period. Observations after this period are excluded from the result."New value: +"End of time range (inclusive). Same format as start_period, and must be greater than or equal to start_period. Observations after this period are excluded from the result."
  8. Changed3 schema fields changed
    • changedInput schema / properties / end_period / description
      Previous value: -"Requested end of time range. Same format as start_period. See start_period note: the API returns the full series; this parameter is passed through but may not filter observations."New value: +"End of time range (inclusive). Same format as start_period. Observations after this period are excluded from the result."
    • changedInput schema / properties / start_period / description
      Previous value: -"Requested start of time range. Format matches the dataflow frequency: YYYY (annual), YYYY-QN (quarterly, e.g. 2023-Q1), YYYY-MM (monthly). Note: the IMF SDMX 3.0 compact JSON endpoint returns the full available series regardless of this parameter — observations outside the requested range may still appear."New value: +"Start of time range (inclusive). Format matches the dataflow frequency: YYYY (annual), YYYY-QN (quarterly, e.g. 2023-Q1), YYYY-MM (monthly). Observations before this period are excluded from the result."
    • changedOutput schema / properties / observations / items / properties / time_period / description
      Previous value: -"Time label, e.g. 2023 or 2023-Q1 or 2023-01."New value: +"Time label as emitted by the upstream API. Annual: YYYY (e.g. 2023). Quarterly: YYYY-QN (e.g. 2023-Q1). Monthly: YYYY-MNN (e.g. 2023-M01, not YYYY-MM). start_period/end_period accept both YYYY-MM and YYYY-MNN for monthly comparisons."
  9. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: the key-to-series expansion behavior (one entry per series in series_metadata), the fact that valid codes can still return no_data, the inclusive period-bound semantics, and the spill-to-DataCanvas behavior. It doesn't contradict annotations. A small gap is that it doesn't explicitly state the result is read-only, but the annotations cover that.

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 dense but well-organized: it front-loads the core purpose, then covers key format, period semantics, and large-result handling. Every sentence earns its place, though it is long. The structure is logical and the most critical usage constraints (key format, period validation) appear early. Slightly verbose but not wasteful.

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?

Given the tool's complexity (8 params, 2 required, rich key-format rules, period semantics, spill behavior) and the presence of an output schema, the description is complete. It covers prerequisites, key construction, period validation, result expansion, no_data behavior, and the large-result workflow. An agent has everything needed to call it correctly and know what to expect.

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 description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds meaning beyond the schema by explaining the key format semantics (dot-separated, + for union, * for wildcard, every position must carry a code), the period-bound inclusivity, and the series_metadata expansion behavior. It reinforces the ISO 3-letter country code requirement. This goes beyond the baseline 3 for full schema coverage.

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: 'Query an IMF SDMX dataflow by dimension key over a time range.' It clearly distinguishes this from siblings by naming the prerequisite (imf_get_database) and the downstream tools (imf_dataframe_describe/query) for large results. The scope is precise and an agent can tell it apart from imf_list_databases or imf_get_database without opening schemas.

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 this tool vs alternatives: it requires imf_get_database first to obtain key_format and valid codes, and it directs large analytical result sets to imf_dataframe_describe then imf_dataframe_query. It also gives concrete examples of key formats and period bounds, so the agent knows exactly when this tool is appropriate and when to hand off.

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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