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Glama

imf-mcp-server

by io.github.cyanheads

Imf Dataframe Describe

imf_dataframe_describe
Read-onlyIdempotent

List DataCanvas tables and columns staged by a prior imf_query_dataset call. Returns each table's name, row count, and column schema (name + DuckDB type). Required before imf_dataframe_query to discover the table and column names for SQL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
canvas_idYesCanvas ID returned by imf_query_dataset whenever staged=true, from automatic spillover or output_mode="canvas".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
tablesNoAll tables registered on this canvas.
canvas_idNoCanvas session ID that was introspected.
table_countNoTotal number of tables on the canvas.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / canvas_id / description
      Previous value: -"Canvas ID returned by imf_query_dataset when results were too large for inline delivery."New value: +"Canvas ID returned by imf_query_dataset whenever staged=true, from automatic spillover or output_mode=\"canvas\"."
  2. 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": [
+      "canvas_id",
+      "tables",
+      "table_count"
+    ]
+  },
+  {
+    "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: `canvas_not_found`: canvas_id does not match any registered DataCanvas session (expired, wrong session, or canvas disabled) Other values are possible when a failure originates below the handler.",
+          "examples": [
+            "canvas_not_found"
+          ],
+          "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: -[
-  "canvas_id",
-  "tables",
-  "table_count"
-]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds meaningful behavioral detail: it returns row counts and column schemas for tables staged by a previous call, and it exposes DuckDB types. There is no contradiction with the annotations.

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?

Two dense sentences with no filler. The action and scope are front-loaded, and the 'Required before imf_dataframe_query' clause gives essential workflow context without redundancy.

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?

For a one-parameter, read-only, idempotent introspection tool with a rich input schema and an output schema present, the description fully equips an agent to select and invoke it correctly. Nothing critical 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 schema provides 100% coverage of the single canvas_id parameter, including its origin conditions (staged=true, automatic spillover, output_mode="canvas"). The description reinforces that context but does not add new parameter-level meaning, so the high-coverage baseline of 3 is appropriate.

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 ('List') and resource ('DataCanvas tables and columns staged by a prior imf_query_dataset call'), then specifies the return content: table name, row count, and column schema with DuckDB types. It also distinguishes itself from imf_dataframe_query by framing this as the discovery step needed before querying.

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?

Clearly defines when to use it: after imf_query_dataset and before imf_dataframe_query, since it is required to discover table/column names for SQL. It does not explicitly list exclusions for other sibling tools like imf_list_databases, but the sequencing and prerequisite context are strong.

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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