SocialGPT

Post metric history

get_post_metrics_history

Read-only

Get one post's metric trajectory over time — views, likes, comments, shares, saves, reach as a time series across window_days (default 90, up to 365). Use it to see how a video accelerated after posting or whether an older post is re-surging. Works for an owned post or any post you've analyzed (use the platform + post_id from list_videos / analyze_post). granularity buckets server-side ('daily' default, 'weekly', or 'raw' for every scrape).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`post_id`	Yes	The post's native post_id (from list_videos / analyze_post).
`platform`	Yes	Target platform — instagram, tiktok, or youtube (case-insensitive).
`granularity`	No	Time-series bucketing: 'daily' (default), 'weekly', or 'raw' (every scrape).	daily
`window_days`	No	Trailing window in days (1–365, default 90; out-of-range values are clamped).

Output Schema

TableJSON Schema

Name	Required	Description
`reason`	No	Present only when series is empty.
`series`	Yes
`post_id`	Yes
`platform`	Yes
`granularity`	Yes
`window_days`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedJul 24, 2026

removedOutput schema / additionalProperties
```
Removed value: -true
```

addedOutput schema / description

Added value: +"Output of `get_post_metrics_history`. `reason`='no_metrics_history'\nappears only when `series` is empty."

addedOutput schema / properties

Added value: +{
+  "granularity": {
+    "enum": [
+      "daily",
+      "weekly",
+      "raw"
+    ],
+    "type": "string"
+  },
+  "platform": {
+    "anyOf": [
+      {
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ]
+  },
+  "post_id": {
+    "type": "string"
+  },
+  "reason": {
+    "anyOf": [
+      {
+        "const": "no_metrics_history",
+        "type": "string"
+      },
+      {
+        "type": "null"
+      }
+    ],
+    "default": null,
+    "description": "Present only when series is empty."
+  },
+  "series": {
+    "items": {
+      "additionalProperties": true,
+      "description": "One time-series point. Metric columns (views, like_count,\nfollower_count, ...) are carried as additional properties; `bucket` is\npresent only for 'daily'/'weekly' granularity.",
+      "properties": {
+        "bucket": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "description": "Bucket label (ISO date or ISO year-week); absent for granularity='raw'."
+        },
+        "timestamp": {
+          "anyOf": [
+            {
+              "format": "date-time",
+              "type": "string"
+            },
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "description": "Snapshot time of this point."
+        }
+      },
+      "type": "object"
+    },
+    "type": "array"
+  },
+  "window_days": {
+    "type": "integer"
+  }
+}

addedOutput schema / required

Added value: +[
+  "platform",
+  "post_id",
+  "window_days",
+  "granularity",
+  "series"
+]

Changed4 schema fields changedJun 24, 2026

addedInput schema / properties / granularity / description

Added value: +"Time-series bucketing: 'daily' (default), 'weekly', or 'raw' (every scrape)."

addedInput schema / properties / platform / description

Added value: +"Target platform — instagram, tiktok, or youtube (case-insensitive)."

addedInput schema / properties / post_id / description

Added value: +"The post's native post_id (from list_videos / analyze_post)."

addedInput schema / properties / window_days / description

Added value: +"Trailing window in days (1–365, default 90; out-of-range values are clamped)."

AddedJun 18, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint and openWorldHint. The description adds behavioral details: granularity is bucketed server-side, window_days default and maximum, and out-of-range values are clamped. These details go beyond what annotations provide.

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 three sentences, front-loading the main purpose. It uses a compact listing of metrics and parameters without unnecessary words. Every sentence adds value.

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, the description does not need to explain return values. It covers purpose, usage, parameters, constraints, and behavior. The tool is fully documented for an AI agent to use 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 description coverage is 100%. The description adds context: post_id source (list_videos/analyze_post), platform listing, granularity server-side behavior, and window_days clamping. This enhances understanding beyond the schema alone.

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's purpose: retrieving a single post's metric trajectory over time, listing specific metrics (views, likes, comments, shares, saves, reach). It distinguishes itself from sibling tools like get_follower_history or get_account_metrics by focusing on one post's time-series data.

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 provides clear usage scenarios ('see how a video accelerated after posting or whether an older post is re-surging') and explains prerequisites (works for owned posts or posts analyzed via list_videos/analyze_post). It does not explicitly exclude alternatives but the context is sufficient.

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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