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Glama

Oecd

by io.github.pipeworx-io

Search Dataflows

search_dataflows
Read-onlyIdempotent

Search OECD's SDMX dataflow registry by keyword. Returns matching dataflow refs ready to pass to fetch_dataset. If results are noisy, refine with a more specific keyword or restrict by agency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 25, max 100)
queryYesKeyword (matches dataflow names + descriptions)
agencyNoOptional agency filter, e.g., OECD.SDD.NAD, OECD.ECO, OECD.ENV

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
returnedYesNumber of results returned (capped by limit)
dataflowsYesMatching dataflows
total_matchedYesTotal number of dataflows matching query

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: +[
+  {
+    "query": "unemployment rate"
+  },
+  {
+    "agency": "OECD.ELS",
+    "limit": 10,
+    "query": "health expenditure"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "dataflows": {
+      "description": "Matching dataflows",
+      "items": {
+        "properties": {
+          "agency": {
+            "description": "Agency identifier",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "flow_ref": {
+            "description": "Complete SDMX dataflow reference",
+            "type": "string"
+          },
+          "id": {
+            "description": "Dataflow ID",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "name": {
+            "description": "Dataflow name",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "version": {
+            "description": "Dataflow version",
+            "type": [
+              "string",
+              "null"
+            ]
+          }
+        },
+        "required": [
+          "flow_ref",
+          "agency",
+          "id",
+          "version",
+          "name"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "returned": {
+      "description": "Number of results returned (capped by limit)",
+      "type": "number"
+    },
+    "total_matched": {
+      "description": "Total number of dataflows matching query",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total_matched",
+    "returned",
+    "dataflows"
+  ],
+  "type": "object"
+}
  2. 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, idempotentHint, etc. Description adds value by stating output nature (dataflow refs) and next-step usage, without contradicting 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 concise sentences: first defines purpose and outcome, second provides refinement advice. No wasted words.

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?

Simple tool with clear purpose, good annotations, and output schema. Description covers purpose, usage, and output relation completely for its complexity.

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?

Schema description coverage is 100% with clear explanations for each parameter. The description does not add significant new meaning beyond 'restrict by agency' which is implied in schema.

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?

Clearly states verb 'Search', resource 'OECD SDMX dataflow registry', and outcome 'Returns matching dataflow refs ready to pass to fetch_dataset'. Distinguishes from sibling fetch_dataset by indicating workflow relationship.

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?

Provides explicit guidance: 'If results are noisy, refine with a more specific keyword or restrict by agency.' Also implies usage before fetch_dataset. No explicit when-not, but context is clear.

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