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by io.github.pipeworx-io
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Get Series

get_series
Read-onlyIdempotent

Fetch any economic time series by ID (e.g., "CPUR0000SA0" for CPI, "LNS14000000" for unemployment). Returns historical data points with dates and values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_apiKeyNoOptional BLS registration key (free, raises the daily cap from ~25 to 500). The gateway supplies a platform key; pass your own only to override.
end_yearNoEnd year as 4-digit string (e.g. "2024"). Optional.
series_idYesBLS series ID (e.g. "CUUR0000SA0" for CPI)
start_yearNoStart year as 4-digit string (e.g. "2020"). Optional.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesTime series data points
totalYesNumber of data points returned. Equal to `returned` — BLS returns every point in the requested year range.
end_yearYesEnd year filter if provided, null otherwise
returnedNoHow many data points are in `data`. Always equal to `total` here; stated so a caller need not assume it.
series_idYesBLS series ID requested
start_yearYesStart year filter if provided, null otherwise
observation_orderNoOrder of the `data` array. BLS returns each series newest-first.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / properties / data / items / properties / value / type
      Previous value: -"number"New value: +[
+  "number",
+  "null"
+]
  2. Changed3 schema fields changed
    • addedOutput schema / properties / observation_order
      Added value: +{
+  "description": "Order of the `data` array. BLS returns each series newest-first.",
+  "enum": [
+    "newest_first"
+  ],
+  "type": "string"
+}
    • addedOutput schema / properties / returned
      Added value: +{
+  "description": "How many data points are in `data`. Always equal to `total` here; stated so a caller need not assume it.",
+  "type": "integer"
+}
    • changedOutput schema / properties / total / description
      Previous value: -"Total number of data points returned"New value: +"Number of data points returned. Equal to `returned` — BLS returns every point in the requested year range."
  3. Changed1 schema field changed
    • addedInput schema / properties / _apiKey
      Added value: +{
+  "description": "Optional BLS registration key (free, raises the daily cap from ~25 to 500). The gateway supplies a platform key; pass your own only to override.",
+  "type": "string"
+}
  4. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "series_id": "CUUR0000SA0"
+  },
+  {
+    "end_year": "2024",
+    "series_id": "LNS14000000",
+    "start_year": "2020"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "Time series data points",
+      "items": {
+        "properties": {
+          "period": {
+            "description": "Period code (e.g., M01 for January)",
+            "type": "string"
+          },
+          "period_name": {
+            "description": "Human-readable period name (e.g., January)",
+            "type": "string"
+          },
+          "value": {
+            "description": "Numeric value for the period",
+            "type": "number"
+          },
+          "year": {
+            "description": "Year of the data point",
+            "type": "string"
+          }
+        },
+        "required": [
+          "year",
+          "period",
+          "period_name",
+          "value"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "end_year": {
+      "description": "End year filter if provided, null otherwise",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "series_id": {
+      "description": "BLS series ID requested",
+      "type": "string"
+    },
+    "start_year": {
+      "description": "Start year filter if provided, null otherwise",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "total": {
+      "description": "Total number of data points returned",
+      "type": "integer"
+    }
+  },
+  "required": [
+    "series_id",
+    "start_year",
+    "end_year",
+    "total",
+    "data"
+  ],
+  "type": "object"
+}
  5. Changed2 schema fields changed
    • removedInput schema / examples
      Removed value: -[
-  {
-    "series_id": "CUUR0000SA0"
-  },
-  {
-    "end_year": "2024",
-    "series_id": "LNS14000000",
-    "start_year": "2020"
-  }
-]
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "data": {
-      "description": "Time series data points",
-      "items": {
-        "properties": {
-          "period": {
-            "description": "Period code (e.g., M01 for January)",
-            "type": "string"
-          },
-          "period_name": {
-            "description": "Human-readable period name (e.g., January)",
-            "type": "string"
-          },
-          "value": {
-            "description": "Numeric value for the period",
-            "type": "number"
-          },
-          "year": {
-            "description": "Year of the data point",
-            "type": "string"
-          }
-        },
-        "required": [
-          "year",
-          "period",
-          "period_name",
-          "value"
-        ],
-        "type": "object"
-      },
-      "type": "array"
-    },
-    "end_year": {
-      "description": "End year filter if provided, null otherwise",
-      "type": [
-        "string",
-        "null"
-      ]
-    },
-    "series_id": {
-      "description": "BLS series ID requested",
-      "type": "string"
-    },
-    "start_year": {
-      "description": "Start year filter if provided, null otherwise",
-      "type": [
-        "string",
-        "null"
-      ]
-    },
-    "total": {
-      "description": "Total number of data points returned",
-      "type": "integer"
-    }
-  },
-  "required": [
-    "series_id",
-    "start_year",
-    "end_year",
-    "total",
-    "data"
-  ],
-  "type": "object"
-}New value: +null
  6. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "Time series data points",
+      "items": {
+        "properties": {
+          "period": {
+            "description": "Period code (e.g., M01 for January)",
+            "type": "string"
+          },
+          "period_name": {
+            "description": "Human-readable period name (e.g., January)",
+            "type": "string"
+          },
+          "value": {
+            "description": "Numeric value for the period",
+            "type": "number"
+          },
+          "year": {
+            "description": "Year of the data point",
+            "type": "string"
+          }
+        },
+        "required": [
+          "year",
+          "period",
+          "period_name",
+          "value"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "end_year": {
+      "description": "End year filter if provided, null otherwise",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "series_id": {
+      "description": "BLS series ID requested",
+      "type": "string"
+    },
+    "start_year": {
+      "description": "Start year filter if provided, null otherwise",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "total": {
+      "description": "Total number of data points returned",
+      "type": "integer"
+    }
+  },
+  "required": [
+    "series_id",
+    "start_year",
+    "end_year",
+    "total",
+    "data"
+  ],
+  "type": "object"
+}
  7. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "series_id": "CUUR0000SA0"
+  },
+  {
+    "end_year": "2024",
+    "series_id": "LNS14000000",
+    "start_year": "2020"
+  }
+]
  8. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the read-only, idempotent, non-destructive profile, and the description adds that the tool returns 'historical data points with dates and values.' This gives useful behavioral context beyond the annotations without contradicting them.

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 sentences, front-loaded with the core action and scope, then concrete examples. Every sentence earns its place and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the fully documented parameters and the presence of an output schema, the description is nearly complete. The only gap is explicit routing guidance relative to sibling tools, which is mild for a simple ID-based fetch tool.

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%, so the schema already documents each parameter thoroughly. The description adds helpful examples of real series IDs, but it does not meaningfully extend the parameter semantics beyond what the schema 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 states a specific verb ('Fetch'), a specific resource ('any economic time series by ID'), and provides concrete examples (CPI and unemployment IDs). This makes the tool's scope immediately clear and inherently distinct from sibling tools like get_cpi or get_unemployment, which target specific series.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes clear that the tool is for fetching a series when you know its ID, but it does not explicitly say when to prefer it over sibling tools or when to use those alternatives. The examples imply coverage of CPI and unemployment, but there is no explicit when/when-not guidance.

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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