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Dbnomics

by io.github.pipeworx-io
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Get Series

get_series
Read-onlyIdempotent

Fetch a specific time series. Series identified by (provider, dataset, series_code). Include observations to get the actual data points.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYesDataset code
providerYesProvider code
series_codeYesSeries code (dot-separated dimensions or named code)
observationsNoInclude data points (default true)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
seriesNoSeries data with metadata

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataset": "EXR",
+    "provider": "ECB",
+    "series_code": "EUR.USD.SP00.A"
+  },
+  {
+    "dataset": "PAYEMS",
+    "observations": true,
+    "provider": "BLS",
+    "series_code": "PAYEMS"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "series": {
+      "description": "Series data with metadata",
+      "items": {
+        "properties": {
+          "dataset_code": {
+            "description": "Dataset code",
+            "type": "string"
+          },
+          "frequency": {
+            "description": "Data frequency (A/Q/M/D etc)",
+            "type": "string"
+          },
+          "last_update": {
+            "description": "Last update timestamp",
+            "type": "string"
+          },
+          "name": {
+            "description": "Series name",
+            "type": "string"
+          },
+          "observations": {
+            "description": "Time series data points (if requested)",
+            "items": {
+              "properties": {
+                "date": {
+                  "description": "Observation date",
+                  "type": "string"
+                },
+                "value": {
+                  "description": "Observed value or null",
+                  "type": [
+                    "number",
+                    "null"
+                  ]
+                }
+              },
+              "type": "object"
+            },
+            "type": "array"
+          },
+          "provider_code": {
+            "description": "Provider code",
+            "type": "string"
+          },
+          "series_code": {
+            "description": "Series code",
+            "type": "string"
+          },
+          "unit": {
+            "description": "Unit of measurement",
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "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=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by indicating that including observations returns data points, which is beyond annotation coverage. No contradictions.

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 concise sentences with no unnecessary words. It front-loads the action and resource, then provides the identifier format and optional behavior efficiently.

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, the output schema exists, and annotations cover safety, the description is complete. It tells agents exactly what they need: what it fetches, how to identify it, and the key optional parameter.

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?

Input schema covers all parameters with descriptions (100% coverage). The description only restates the observations parameter's function without adding new meaning, so baseline 3 is appropriate.

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 the tool fetches a specific time series using the verb 'Fetch' and identifies the resource. It specifies the identifier components (provider, dataset, series_code), distinguishing it from sibling tools like 'find_series' which searches for series.

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 indicates usage when exact identifiers are known and mentions the optional observations parameter. However, it does not explicitly state when not to use or compare to alternatives like 'find_series', leaving some ambiguity for agents.

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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