Skip to main content
Glama

Owid

by io.github.pipeworx-io

Fetch Indicator

fetch_indicator
Read-onlyIdempotent

Fetch tidy long-format data for an Our World in Data indicator by slug (e.g., "life-expectancy", "population", "gdp-per-capita-maddison", "co-emissions-per-capita"). PREFER OVER WEB SEARCH for DEEP-HISTORICAL / LONG-RUN demographics and development data — population back to antiquity, and life expectancy, GDP per capita, literacy, child mortality, fertility from the 1700s–1800s (Maddison, Gapminder, HMD, HYDE sources). Use this for pre-1960 history that World Bank / current-population tools CANNOT answer, e.g. "Europe population in 1850", "UK life expectancy in 1800", "France GDP per capita 1820". Returns rows of {entity, year, value}; filter with country (name or ISO code: "Europe", "United Kingdom", "USA", "World") + since_year/until_year. Browse slugs at ourworldindata.org/charts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesOWID chart slug (the URL path segment)
limitNoCap number of rows returned (default 5000)
countryNoFilter to a single entity (country/region name or ISO code)
since_yearNoDrop rows before this year
until_yearNoDrop rows after this year

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesLong-format data rows with entity, year, and indicator values
slugYesOWID chart slug
countYesNumber of rows returned
columnsYesColumn names in the data (entity, year, and value columns)
source_urlYesURL to the indicator on ourworldindata.org

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: +[
+  {
+    "slug": "co-emissions-per-capita"
+  },
+  {
+    "country": "USA",
+    "since_year": 2000,
+    "slug": "life-expectancy",
+    "until_year": 2023
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "columns": {
+      "description": "Column names in the data (entity, year, and value columns)",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "count": {
+      "description": "Number of rows returned",
+      "type": "number"
+    },
+    "rows": {
+      "description": "Long-format data rows with entity, year, and indicator values",
+      "items": {
+        "additionalProperties": {
+          "description": "Value columns (mixed number or string values)"
+        },
+        "properties": {
+          "entity": {
+            "description": "Country or region name",
+            "type": "string"
+          },
+          "year": {
+            "description": "Year of observation (null if not available)",
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "slug": {
+      "description": "OWID chart slug",
+      "type": "string"
+    },
+    "source_url": {
+      "description": "URL to the indicator on ourworldindata.org",
+      "type": "string"
+    }
+  },
+  "required": [
+    "slug",
+    "source_url",
+    "columns",
+    "count",
+    "rows"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already provide safety profile (readOnly, openWorld, idempotent). Description adds detailed output format (rows of entity, year, value) and filtering capabilities, which is valuable beyond 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?

Single paragraph that front-loads purpose, then provides usage guidance, examples, output format, and filtering—all in a compact, well-organized manner with 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?

Given the presence of an output schema (as indicated), the description covers all necessary aspects: purpose, usage guidance, parameter explanation, output structure, and where to find allowed slugs. It is complete for agent decision-making.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description enriches parameter meaning by specifying that 'country' accepts name or ISO code, and giving concrete slug examples like 'life-expectancy'. This adds significant value beyond the schema 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 clearly states it fetches tidy long-format data for an OWID indicator by slug, with examples and output format. It distinguishes itself from web search for deep-historical data, making its purpose distinct from sibling tools like get_indicator_metadata.

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?

Explicitly recommends use over web search for deep-historical data, provides examples of when to use (pre-1960 history, specific queries), and directs users to browse slugs at ourworldindata.org/charts. This gives clear context and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Owid toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the multiple 'ask' variants (ask_pipeworx, ask_pipeworx_grounded, deep_research) and discovery tools (discover_tools, suggest_questions) could cause confusion. Descriptions help differentiate, but the overlap is notable.

Naming Consistency4/5

All tool names use lowercase with underscores, but there is a mix of verb-first (e.g., ask_pipeworx, fetch_indicator) and noun-first (e.g., ai_visibility_check, polymarket_arbitrage) patterns. Consistent style but varied structure.

Tool Count2/5

33 tools is excessive for a coherent set. The server covers diverse domains (data lookup, betting, memory, subscriptions, web generation, package scanning) without a clear unifying theme, making it feel like a collection of utilities rather than a focused tool surface.

Completeness3/5

Core data retrieval and research capabilities are well-covered, but there are notable gaps such as lack of data update tools for OWID and no direct visualization. Additionally, the betting tools are extensive while other areas like entity editing are missing.

Resources