TGMCharts

Raw financial statement (SEC EDGAR)

get_financial_statement

Read-onlyIdempotent

Use for line items as filed with the SEC (revenue, net income, cash flow, dividends paid, debt…) by year or quarter. Raw as-filed statement rows (income statement, balance sheet, or cash flow) sourced exclusively from SEC EDGAR XBRL company facts — public domain, latest-filed per period, honest nulls (never zero-filled, never vendor-backfilled). Derived quarters are disclosed. Token-frugal default: 5 fiscal years without per-value provenance; set include_provenance for the winning us-gaap tag + accession per value, or use https://tgmcharts.com/api/v1/statements/{symbol}/{statement} for full depth (30y, CSV).

Input Schema

Name	Required	Description
`years`	No	Trailing fiscal years of rows (default 5; quarterly serves 4×years)
`format`	No	json (default): the full JSON as text. markdown: a compact table or list, fewer tokens. structuredContent is the full JSON either way.
`period`	No	annual (default) or quarterly
`symbol`	Yes	Ticker symbol, e.g. AAPL
`statement`	Yes	Which statement
`include_provenance`	No	Include per-value provenance (us-gaap tag, accession, filed date). Default false to stay token-frugal.

Output Schema

Name	Required	Description	Default
`cik`	Yes
`asOf`	Yes
`rows`	Yes
`links`	Yes
`period`	Yes
`source`	Yes
`symbol`	Yes
`concepts`	Yes
`currency`	Yes
`statement`	Yes
`entityName`	Yes
`methodology`	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": {
+    "asOf": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "cik": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "concepts": {
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "key": {
+            "type": "string"
+          },
+          "label": {
+            "type": "string"
+          },
+          "unit": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "key",
+          "label",
+          "unit"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "currency": {
+      "type": "string"
+    },
+    "entityName": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "links": {
+      "additionalProperties": {},
+      "properties": {
+        "docs": {
+          "type": "string"
+        },
+        "openapi": {
+          "type": "string"
+        },
+        "page": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "page",
+        "docs",
+        "openapi"
+      ],
+      "type": "object"
+    },
+    "methodology": {
+      "type": "string"
+    },
+    "period": {
+      "type": "string"
+    },
+    "rows": {
+      "anyOf": [
+        {
+          "items": {
+            "additionalProperties": {},
+            "properties": {
+              "filed": {
+                "type": [
+                  "string",
+                  "null"
+                ]
+              },
+              "fiscalPeriod": {
+                "type": "string"
+              },
+              "fiscalYear": {
+                "type": "number"
+              },
+              "form": {
+                "type": [
+                  "string",
+                  "null"
+                ]
+              },
+              "metrics": {
+                "additionalProperties": {
+                  "type": [
+                    "number",
+                    "null"
+                  ]
+                },
+                "propertyNames": {
+                  "type": "string"
+                },
+                "type": "object"
+              },
+              "periodEnd": {
+                "type": "string"
+              },
+              "periodStart": {
+                "type": [
+                  "string",
+                  "null"
+                ]
+              }
+            },
+            "required": [
+              "fiscalYear",
+              "fiscalPeriod",
+              "periodStart",
+              "periodEnd",
+              "form",
+              "filed",
+              "metrics"
+            ],
+            "type": "object"
+          },
+          "type": "array"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "source": {
+      "type": "string"
+    },
+    "statement": {
+      "type": "string"
+    },
+    "symbol": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "symbol",
+    "entityName",
+    "cik",
+    "statement",
+    "period",
+    "currency",
+    "source",
+    "asOf",
+    "methodology",
+    "concepts",
+    "rows",
+    "links"
+  ],
+  "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 cover only the safety profile (read-only, idempotent), yet the description adds substantial behavior: exclusive SEC EDGAR XBRL sourcing, public-domain status, latest-filed-per-period semantics, honest nulls (never zero-filled or vendor-backfilled), disclosed derived quarters, and default token-frugal behavior. This is exactly the kind of context annotations cannot carry.

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 the use case and dense with signal; nearly every clause earns its place (sourcing, null policy, provenance, CSV fallback). It is a single long paragraph, which slightly hurts scannability, but there is little waste.

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 an output schema present, the description need not explain return shapes, and it fills the remaining gaps: data provenance, null semantics, quarterly derivation, and provenance opt-in. An agent has everything required to call it correctly.

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 coverage is 100%, so the baseline is 3, but the description adds real meaning: it explains the 5-year default, what include_provenance unlocks (winning us-gaap tag + accession per value), and offers a deeper alternative endpoint. That goes beyond the schema's per-parameter field 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?

States a precise verb+resource ('line items as filed with the SEC... by year or quarter') and enumerates concrete content (revenue, net income, cash flow, dividends paid, debt). It also characterizes the scope as raw as-filed statement rows from EDGAR, which visibly separates it from derived-metric siblings like get_metric_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 opening 'Use for line items as filed with the SEC' gives clear context for when to reach for this tool, and the token-frugal default plus the CSV depth alternative gives explicit guidance. It stops short of naming the sibling tools (e.g. get_metric_series) it should be preferred over, so an agent must infer the split.

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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