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Glama

Oecd

by io.github.pipeworx-io

Fetch Dataset

fetch_dataset
Read-onlyIdempotent

Fetch tidy rows from any OECD dataflow. flow_ref examples: "OECD.SDD.NAD,DSD_NAMAIN1@DF_QNA_EXPENDITURE_GROWTH,1.0". The key string is a dot-separated dimension filter (e.g., "USA.....Q" — leave empty to fetch everything). Use start/end periods like "2020-Q1" or "2020". Returns labeled rows; OECD enforces a result-size limit and may truncate broad queries — narrow with key dimensions or shorter time ranges.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoDot-separated dimension key (or empty for all)
limitNoCap rows returned (default 5000)
flow_refYesSDMX dataflow reference
end_periodNoInclusive end period
start_periodNoe.g., "2020", "2020-Q1", "2020-01"

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesData rows with column names as keys
countYesNumber of data rows returned
columnsYesCSV column headers
flow_refYesThe requested SDMX dataflow reference
truncatedYesTrue if result was limited by row cap
source_urlYesURL to OECD data explorer for this flow

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "end_period": "2023-Q4",
+    "flow_ref": "OECD.SDD.NAD,DSD_NAMAIN1@DF_QNA_EXPENDITURE_GROWTH,1.0",
+    "key": "USA.....Q",
+    "start_period": "2020-Q1"
+  },
+  {
+    "end_period": "2024",
+    "flow_ref": "OECD.ELS,DSD_LFS@DF_ALFS_POPULATION,1.0",
+    "key": "",
+    "start_period": "2022"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "columns": {
+      "description": "CSV column headers",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "count": {
+      "description": "Number of data rows returned",
+      "type": "number"
+    },
+    "flow_ref": {
+      "description": "The requested SDMX dataflow reference",
+      "type": "string"
+    },
+    "rows": {
+      "description": "Data rows with column names as keys",
+      "items": {
+        "additionalProperties": {
+          "type": "string"
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "source_url": {
+      "description": "URL to OECD data explorer for this flow",
+      "type": "string"
+    },
+    "truncated": {
+      "description": "True if result was limited by row cap",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "flow_ref",
+    "source_url",
+    "columns",
+    "truncated",
+    "count",
+    "rows"
+  ],
+  "type": "object"
+}
  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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context that OECD enforces a result-size limit and may truncate broad queries, which is valuable beyond annotations. It also states that the tool returns labeled rows. No contradiction 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 plus an example, front-loaded with the main purpose. Every sentence adds value: the first states the purpose, the second explains parameters and limitations. No wasted words. The structure is efficient.

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 presence of an output schema, the description does not need to explain return values. It covers the essential aspects: what it fetches, how to specify parameters, and potential truncation behavior. The description is fully adequate for a fetch tool with good annotations.

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 baseline is 3. The description adds meaning by explaining the key string format with an example and providing guidance on period formatting. It reinforces the schema descriptions and adds examples, improving clarity beyond what schema alone provides.

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 'Fetch tidy rows from any OECD dataflow', specifying the verb 'fetch' and the resource 'tidy rows from OECD dataflows'. The inclusion of examples for flow_ref and key string further clarifies the tool's purpose. It is easily distinguishable from siblings like 'search_dataflows' which searches for dataflows.

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 provides guidance on usage by explaining the key string format and period parameters. It also warns about OECD's result-size limit and advises narrowing broad queries. While it does not explicitly compare to alternatives, the context is clear for fetching data vs searching for dataflows.

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 near-duplicate lookup and prediction-market tools make selection ambiguous: ask_pipeworx_beta deliberately mirrors ask_pipeworx, and the five polymarket_* tools plus bet_research all target the same general 'should I bet / where is the edge' use case. The descriptions are detailed, but at the set level an agent must read extensive disambiguation essays to avoid picking the wrong tool.

Naming Consistency3/5

The set is uniformly snake_case, and subfamilies like ask_pipeworx*, polymarket_*, and subscribe/unsubscribe are internally consistent. However, conventions vary widely: verb_noun (fetch_dataset, validate_claim), noun phrases (entity_profile, bet_research), bare verbs (remember, recall, forget), and prefix-branded meta tools (pipeworx_feedback, pipeworx_trending) all coexist without a single predictable pattern.

Tool Count2/5

34 tools for a server nominally called 'Oecd' vastly exceeds the scope implied by the name and crosses the 25+ too-many threshold. Many tools belong to unrelated domains such as Polymarket arbitrage, npm dependency scanning, llms.txt generation, and AI visibility audits, making the set feel like a broad dumping ground rather than a focused tool server.

Completeness4/5

Within its sprawling domains the tool surface is fairly complete: lookup, grounded verification, deep research, entity resolution/profile/comparison, memory, subscriptions, alerts, and OECD dataflow search/list/fetch are all represented. There are minor gaps such as lack of direct OECD metadata descriptions or deeper navigation of the 5,708 underlying tools, but most workflows can be completed without dead ends.

Resources