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Imf

by io.github.pipeworx-io

Search Indicators

search_indicators
Read-onlyIdempotent

Discover what a dataset accepts: its dimension names in key order, and the codes available for each, filtered by query. Use this before get_data — e.g. search_indicators({dataset:"CPI", query:"united states"}) finds COUNTRY=USA. Without a query it returns the dimension list and a sample of codes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoOptional term matched against code ids and names, e.g. "united states", "GDP", "monthly".
datasetYesDataset id from get_datasets, e.g. "CPI".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoLowercase search query term used for filtering
structureYesIMF DataStructure containing indicator code dimensions and metadata

Schema Changelog

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

  1. Changed5 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "database_id": "IFS",
-    "query": "GDP"
-  },
-  {
-    "database_id": "BOP"
-  }
-]New value: +[
+  {
+    "dataset": "CPI",
+    "query": "united states"
+  },
+  {
+    "dataset": "CPI"
+  }
+]
    • removedInput schema / properties / database_id
      Removed value: -{
-  "description": "IMF database ID to search within, e.g. \"IFS\"",
-  "type": "string"
-}
    • addedInput schema / properties / dataset
      Added value: +{
+  "description": "Dataset id from get_datasets, e.g. \"CPI\".",
+  "type": "string"
+}
    • changedInput schema / properties / query / description
      Previous value: -"Optional search term to filter indicators, e.g. \"GDP\", \"inflation\", \"trade\""New value: +"Optional term matched against code ids and names, e.g. \"united states\", \"GDP\", \"monthly\"."
    • changedInput schema / required
      Previous value: -[
-  "database_id"
-]New value: +[
+  "dataset"
+]
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

The annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description doesn't need to repeat that. It adds useful behavioral context beyond annotations: the effect of a query (filtering codes) and the behavior when no query is provided (returns dimension list and a sample of codes). No contradictions with 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?

The description is two sentences long, front-loaded with the core purpose, and includes a concrete example. Every sentence contributes meaning, and there is no redundancy with the schema or annotations.

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 simplicity (2 parameters, output schema present, strong annotations), the description fully covers what an agent needs to know: what it discovers, how to use it before get_data, and the difference between query and no-query behavior. No critical gaps.

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 the baseline is 3. The description adds value by explaining the query parameter's filtering behavior and illustrating with an example (search_indicators({dataset:"CPI", query:"united states"}) finds COUNTRY=USA). It also clarifies the effect of omitting the optional query parameter, going slightly beyond the schema's property descriptions.

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 states exactly what the tool does: 'Discover what a dataset accepts: its dimension names in key order, and the codes available for each, filtered by query.' It uses a specific verb (discover) and resource (dataset dimensions/codes), and clearly differentiates from get_data by saying 'Use this before get_data'.

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?

Provides explicit usage context: 'Use this before get_data' and gives a concrete example with the CPI dataset. This tells the agent when to invoke it (as a preliminary exploration step) and distinguishes it from the data-fetching sibling tool.

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