TGMCharts

Metric history series

get_metric_series

Read-onlyIdempotent

Use for a metric's history or trend (is the P/E high versus its past? how have margins moved?). Historical series for a TGMCharts-computed metric (daily valuation/yield lines, rolling TTM flows, quarterly margins). Points are even-time downsampled to max_points (default 120) to stay token-frugal — raw density is available at https://tgmcharts.com/api/v1/series/{symbol}/{metric}. points:null = intentionally withheld (sector-inappropriate, message included); [] = no stored history.

Input Schema

Name	Required	Description
`years`	No	Trailing window in years (default: full stored history)
`format`	No	json (default): the full JSON as text. markdown: a compact table or list, fewer tokens. structuredContent is the full JSON either way.
`metric`	Yes	Series-tier metric slug (see list_metrics)
`symbol`	Yes	Ticker symbol, e.g. KO
`max_points`	No	Downsample cap (default 120)

Output Schema

Name	Required	Description	Default
`asOf`	Yes
`name`	Yes
`unit`	Yes
`basis`	Yes
`label`	Yes
`links`	Yes
`years`	Yes
`metric`	Yes
`points`	Yes
`symbol`	Yes
`methodology`	Yes
`applicability`	Yes
`downsampledTo`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 15, 2026

addedInput schema / properties / format

Added value: +{
+  "description": "json (default): the full JSON as text. markdown: a compact table or list, fewer tokens. structuredContent is the full JSON either way.",
+  "enum": [
+    "json",
+    "markdown"
+  ],
+  "type": "string"
+}

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": {},
+  "properties": {
+    "applicability": {
+      "anyOf": [
+        {
+          "const": "applicable",
+          "type": "string"
+        },
+        {
+          "additionalProperties": {},
+          "properties": {
+            "message": {
+              "type": "string"
+            },
+            "tone": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "tone",
+            "message"
+          ],
+          "type": "object"
+        }
+      ]
+    },
+    "asOf": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "basis": {
+      "type": "string"
+    },
+    "downsampledTo": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "label": {
+      "type": "string"
+    },
+    "links": {
+      "additionalProperties": {},
+      "properties": {
+        "chart": {
+          "type": [
+            "string",
+            "null"
+          ]
+        },
+        "docs": {
+          "type": "string"
+        },
+        "openapi": {
+          "type": "string"
+        },
+        "page": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "chart",
+        "page",
+        "docs",
+        "openapi"
+      ],
+      "type": "object"
+    },
+    "methodology": {
+      "type": "string"
+    },
+    "metric": {
+      "type": "string"
+    },
+    "name": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "points": {
+      "anyOf": [
+        {
+          "items": {
+            "additionalProperties": {},
+            "properties": {
+              "date": {
+                "type": "string"
+              },
+              "value": {
+                "type": "number"
+              }
+            },
+            "required": [
+              "date",
+              "value"
+            ],
+            "type": "object"
+          },
+          "type": "array"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "symbol": {
+      "type": "string"
+    },
+    "unit": {
+      "type": "string"
+    },
+    "years": {
+      "type": [
+        "number",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "symbol",
+    "name",
+    "metric",
+    "label",
+    "unit",
+    "basis",
+    "methodology",
+    "asOf",
+    "years",
+    "points",
+    "applicability",
+    "links",
+    "downsampledTo"
+  ],
+  "type": "object"
+}

First observedOct 12, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly/idempotent/non-destructive), and the description layers on real behavioral detail beyond them: even-time downsampling to max_points with a 120 default, the raw-density URL for full fidelity, and the exact meaning of points:null (intentionally withheld, message included) versus [] (no stored history). Those are non-obvious runtime traits an agent could not infer from structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose then behavioral detail, and every clause carries information (downsampling, raw fallback, null-vs-empty semantics). It is dense — several parentheticals stacked in one paragraph — but not padded.

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?

With a full output schema available, the description needn't explain return values, yet it still clarifies the two ambiguous return states (null vs []). Combined with the annotation coverage and the sampling contract, nothing an agent needs to call this correctly is missing.

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 the baseline is 3; the description nonetheless adds useful semantics around max_points (downsampling cap, token-frugality rationale) and the escape hatch URL for un-downsampled density. It goes modestly beyond restating the schema, though format's json-vs-markdown tradeoff is largely left to the 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?

States a specific verb+resource ('Historical series for a TGMCharts-computed metric') and enumerates the kinds of lines returned (daily valuation/yield, rolling TTM flows, quarterly margins). The motivating examples ('is the P/E high versus its past?') make the intent unambiguous relative to siblings like get_stock_summary or get_chart.

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?

Opens with an explicit when-to-use framing ('Use for a metric's history or trend') plus concrete question examples that tell an agent what class of query belongs here. It does not, however, name an alternative tool or state when-not to use it, so the routing guidance stops short of full disambiguation.

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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