Nuntior

Cross-Network Analytics & Live Social Listening

analytics_query

Read-onlyIdempotent

Query cross-network engagement analytics (account_overview, post_metrics, audience_growth), compute AI-recommended optimal posting windows (best_times), or execute live real-time social listening (social_listen) across Bluesky, Hacker News, Dev.to, Reddit, and X. Use social_listen to discover live community threads, competitor mentions, and high-intent buyer questions matching any keyword query. Returns aggregated engagement metrics, X Smart Link cost savings, optimal CET/UTC posting slots, or live upstream social signals with direct thread URLs and author handles.

Input Schema

Name	Required	Description	Default
`query`	No	Search keyword, brand name, competitor handle, or buyer-intent phrase when running live `action: 'social_listen'` (e.g. 'MCP server social media API' or 'n8n AI agent workflow').
`action`	Yes	Analytics or listening mode: 'account_overview' for cross-network KPIs, 'post_metrics' for individual post performance, 'audience_growth' for follower velocity, 'best_times' for highest-lift publishing windows, or 'social_listen' for live keyword/intent discovery.
`postId`	No	Optional post identifier (`post_...`) when inspecting granular metrics via `action: 'post_metrics'`.
`platform`	No	Optional social network filter to scope metrics or listening to a single platform.
`profileId`	No	Brand profile identifier (`prof_...`) to aggregate analytics for.	prof_allometrik
`periodDays`	No	Lookback window in days (e.g. 7, 30, 90) for aggregating impressions, engagements, and follower growth.

Schema Changelog

Changes observed during successful MCP inspections.

Changed9 schema fields changedOct 26, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

addedInput schema / properties / action / description

Added value: +"Analytics or listening mode: 'account_overview' for cross-network KPIs, 'post_metrics' for individual post performance, 'audience_growth' for follower velocity, 'best_times' for highest-lift publishing windows, or 'social_listen' for live keyword/intent discovery."

addedInput schema / properties / periodDays / description

Added value: +"Lookback window in days (e.g. 7, 30, 90) for aggregating impressions, engagements, and follower growth."

addedInput schema / properties / platform / description

Added value: +"Optional social network filter to scope metrics or listening to a single platform."

changedInput schema / properties / platform / enum

Previous value: -[
-  "x",
-  "linkedin",
-  "instagram",
-  "facebook",
-  "threads",
-  "tiktok",
-  "youtube",
-  "bluesky",
-  "mastodon",
-  "farcaster",
-  "reddit",
-  "pinterest",
-  "google_business",
-  "telegram",
-  "discord",
-  "ghost",
-  "wordpress",
-  "webflow"
-]New value: +[
+  "x",
+  "linkedin",
+  "bluesky",
+  "instagram",
+  "facebook",
+  "threads",
+  "tiktok",
+  "youtube",
+  "reddit",
+  "pinterest",
+  "mastodon",
+  "telegram",
+  "discord",
+  "google_business",
+  "ghost",
+  "wordpress",
+  "webflow",
+  "whatsapp"
+]

addedInput schema / properties / postId / description

Added value: +"Optional post identifier (`post_...`) when inspecting granular metrics via `action: 'post_metrics'`."

addedInput schema / properties / profileId / default
```
Added value: +"prof_allometrik"
```

addedInput schema / properties / profileId / description

Added value: +"Brand profile identifier (`prof_...`) to aggregate analytics for."

changedInput schema / properties / query / description

Previous value: -"Keyword or brand query for live social_listen"New value: +"Search keyword, brand name, competitor handle, or buyer-intent phrase when running live `action: 'social_listen'` (e.g. 'MCP server social media API' or 'n8n AI agent workflow')."

First observedOct 26, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint/idempotentHint/openWorldHint), so the bar is lower. The description adds real value by describing what comes back — aggregated engagement metrics, X Smart Link cost savings, CET/UTC posting slots, or live signals with direct thread URLs and author handles — which the annotations do not disclose.

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?

Two dense sentences, front-loaded with the action modes and their purpose. The trailing sentence on return values is useful but partially duplicates what the leading sentences already implied about outputs.

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?

With no output schema, the description compensates by sketching the return shape for each action, and it names the five modes and covered networks. It omits nothing critical for calling the tool, though per-action parameter requirements (e.g., that query matters only for social_listen) are left to the schema.

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 every parameter (including the action enum and query) is already documented in the schema and the description's restatement adds little. Baseline 3 applies; the description neither adds syntax/format detail nor introduces confusion.

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 names specific verbs (query, compute, execute) and enumerates the five action modes alongside the covered networks, so an agent knows exactly what the tool does and how it differs from siblings like traffic_analytics. The distinction between analytics, best_times, and social_listen is spelled out rather than implied.

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?

It explicitly routes one mode ('Use `social_listen` to discover live community threads, competitor mentions, and high-intent buyer questions'), which is clear when-to-use guidance for that action. It stops short of naming a sibling alternative or stating when not to use this tool versus traffic_analytics, so it falls short of a 5.

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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