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Glama

Imf

by io.github.pipeworx-io

Get Datasets

get_datasets
Read-onlyIdempotent

List or search the IMF's datasets — CPI, WEO (World Economic Outlook), BOP (Balance of Payments), IMTS (trade in goods), GFS (government finance), MFS (monetary and financial), FSI (financial soundness), commodity prices and ~200 more. Returns the dataset id to pass to get_data and search_indicators, plus its name and description. Pass query to filter by name, e.g. "inflation", "trade", "government".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax datasets to return, 1-250 (default 50).
queryNoOptional filter matched against dataset id, name and description (e.g. "inflation", "trade").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {}
-]New value: +[
+  {},
+  {
+    "limit": 50,
+    "query": "price"
+  }
+]
    • addedInput schema / properties / limit
      Added value: +{
+  "description": "Max datasets to return, 1-250 (default 50).",
+  "type": "number"
+}
    • addedInput schema / properties / query
      Added value: +{
+  "description": "Optional filter matched against dataset id, name and description (e.g. \"inflation\", \"trade\").",
+  "type": "string"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds value beyond annotations by explaining that the tool returns a dataset id, name, and description, and that this id is meant to be passed to get_data and search_indicators. This downstream usage context is useful behavioral information not present in the schema.

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 well-organized, starting with the core action ('List or search'), followed by concrete examples of datasets, return semantics, and query usage. Every sentence carries meaningful information, and the examples are illustrative without being redundant. It's appropriately sized for the tool's simplicity.

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 low complexity (two optional parameters, output schema present), the description fully covers the essential aspects: what it does, what it returns, how to refine results, and how the output connects to other tools. The output schema handles return structure, so the description doesn't need to explain that further.

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% with both parameters described. The description adds practical guidance by giving example query values ('inflation', 'trade', 'government') and explaining the filter's scope against id, name, and description. This goes beyond the schema's literal parameter descriptions and helps the agent construct effective queries.

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 states the tool's purpose with a specific verb and resource: 'List or search the IMF's datasets'. It also distinguishes itself from sibling tools by noting it returns the dataset id to pass to get_data and search_indicators, making its role as a discovery entry point explicit.

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 description implies when to use this tool (to discover datasets before using get_data or search_indicators) and provides concrete examples of query usage. It doesn't explicitly say when not to use it or name alternative tools, but the context is clear enough for an agent to infer the appropriate usage scenario.

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

A3.9/5.0
Disambiguation2/5

Several natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) have heavily overlapping purposes, and ask_pipeworx_beta currently behaves identically to ask_pipeworx. The five Polymarket tools also have subtle boundaries, though the IMF, memory, and subscription clusters are clearly separated.

Naming Consistency4/5

Most tools follow a clean snake_case verb_noun pattern (get_data, resolve_entity, subscribe, compare_entities). Minor deviations exist: noun-first names like entity_profile and ai_visibility_check, brand-prefixed names like pipeworx_feedback and pipeworx_trending, and ask_pipeworx_beta using a suffix instead of an underscore.

Tool Count2/5

34 tools is well above the range that remains easily navigable, and the count is inflated by many meta-tools, overlapping query entry points, and five distinct Polymarket tools. The server is named Imf, yet it also carries npm dependency scanning, llms.txt generation, AI visibility checks, and prediction-market tooling, making the scope feel unfocused.

Completeness3/5

Subdomain lifecycles are reasonably covered: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and data access has discovery, lookup, grounding, and research paths. However, the overall domain is so broad that a complete surface is hard to define, and the IMF-specific portion is thin (only get_data, get_datasets, and search_indicators).

Resources