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News and Media Mention Monitor MCP Server

News and Media Mention Monitor MCP Server

by mambalabsdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

News and Media Mention Monitor MCP Server

npm version license

An MCP server for the Mamba Labs News and Media Mention Monitor actor on Apify. Give it company names, brands, people, or topics. It reads Google News for each one and returns one row per article: headline, outlet, date, the publisher's own URL and domain, and a headline sentiment label. With only_new on, a repeat call with the same state_key returns only articles it has not returned before.

What's Inside

Related MCP server: Google News22 MCP Server

What it does

Each news.google.com link is resolved to the article's URL on the publisher's site. An article found by two of your search terms comes back once. When a run returns no article, one free summary row says why: nothing matched, everything was returned before, or Google News did not answer. All of the work runs on Apify. This package is a thin client that starts the actor run, waits for it, and hands back the dataset rows.

Quick start

You need Node.js 18 or newer and an Apify account with an API token.

Add this to your Claude Desktop config (claude_desktop_config.json), Cursor, or any MCP client:

{
  "mcpServers": {
    "mamba-news-media-mention-monitor": {
      "command": "npx",
      "args": [
        "-y",
        "@mambalabsdev/mcp-news-media-mention-monitor"
      ],
      "env": {
        "APIFY_TOKEN": "your-apify-token"
      }
    }
  }
}

Restart the client. The monitor_news_mentions tool appears.

Prerequisites

Example prompts

  • "What did the news say about Gong and Salesloft this week?"

  • "Find news articles about our company from the last 30 days and list the outlets."

  • "Any negative headlines about Acme Robotics since my last check?"

Tool

monitor_news_mentions. One tool. Example arguments:

{
  "queries": ["\"Acme Robotics\""],
  "since_days": "7",
  "state_key": "acme-weekly"
}

Inputs

Every buyer facing input of the actor, unchanged. Omitted inputs take the actor's own defaults. Numbers and yes or no values are sent as strings.

Input

Type

Description

queries

array

1 to 50 search terms. Required

exclude_terms

array

Up to 20 words to leave out

since_days

string

Days back, 1 to 90. Default 7

country

string

Google News country edition. Default US

language

string

Language code. Default en

resolve_publisher_url

string

true (default) or false

only_new

string

true (default) or false

state_key

string

Name for this watch list

max_articles

string

Per search term, 1 to 100. Default 50

max_charge_usd

string

Optional cap on the run's cost

Pricing

Pay per event on Apify, billed to your Apify account. Prices in USD.

Event

Free plan

Bronze

Silver

Gold

article-row, per article returned

$0.0023

$0.002185

$0.00207

$0.001955

apify-actor-start, per run start

$0.0015

$0.0015

$0.0015

$0.0015

An article skipped by only_new is not charged. A summary row is free.

Your keys

The actor needs no key and holds none. Proxy traffic adds nothing to your price: Mamba Labs pays the platform costs of the run.

Full actor documentation

apify.com/mambalabs/news-media-mention-monitor

License

MIT. See LICENSE.

Available Tools

1 tool
monitor_news_mentionsMonitor news mentionsA
Read-only

Find news articles about companies, brands, people, or topics from Google News. Give queries (1 to 50 search terms; quotes for an exact phrase) and optionally since_days, country, language, only_new, and state_key. Returns one row per article: title, outlet, outlet_domain, published_at, publisher_url and publisher_domain (the article on the publisher's own site), google_url, sentiment (headline only), is_new, and matched_queries. With only_new true, articles returned by an earlier call with the same state_key are skipped and not charged. When no article is returned, one free summary row says why. A run that does not succeed is returned as an error with its run ID. Read-only; requires an APIFY_TOKEN; Apify charges per article returned plus a run start fee.

NameRequiredDescriptionDefault
countryNoGoogle News country edition. Default US.
queriesYes1 to 50 search terms: company names, brands, people, or topics. Put a phrase in double quotes for an exact match.
languageNoLanguage code of the edition, for example en, de, or pt-419. Default en.
only_newNotrue (default): skip articles an earlier run with the same state_key returned.
state_keyNoName for this watch list, for example acme-weekly.
since_daysNoDays back, 1 to 90. Default 7.
max_articlesNoMost articles per search term, 1 to 100, newest first. Default 50.
exclude_termsNoUp to 20 words to leave out of every search.
max_charge_usdNoOptional cap on this run's cost in USD.
resolve_publisher_urlNotrue (default): resolve each article to the publisher's own URL.

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and destructiveHint=false; the description adds substantially more: APIFY_TOKEN requirement, per-article plus run-start billing, max_charge_usd cost capping, the fact that skipped articles are not charged, a free summary row for empty results, and error returns carrying a run ID. This is exactly the kind of context annotations cannot convey.

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?

The opening sentence leads with the core action and the operational details follow in a logical order. The enumeration of return fields is long but each field carries information an agent needs for output handling.

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?

There is no output schema, and the description compensates by enumerating the returned row fields (title, outlet, published_at, publisher_url, google_url, sentiment, is_new, matched_queries) plus the empty-result summary row and the error shape. An agent has everything needed to call and interpret results.

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 coverage is 100%, so the baseline is 3. The description restates the roles of queries, since_days, country, language, only_new, and state_key but omits max_articles, exclude_terms, max_charge_usd, and resolve_publisher_url, adding little beyond 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 and resource ('Find news articles about companies, brands, people, or topics from Google News'), naming the source explicitly. There are no sibling tools to disambiguate against, so the definition is self-sufficient.

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?

Explains how to shape queries (1-50 terms, quotes for exact phrases) and the intended incremental-watch workflow via only_new plus state_key. It does not discuss when the tool is inappropriate (e.g., non-news sources), but with no siblings there is no alternative to route to.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.0
    • First observedmonitor_news_mentions
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.3/5.0

Scored across 1 tool

Disambiguation5/5

There is only one tool, so there is no possibility of confusing it with another. Its purpose—finding news mentions for given queries—is clear from the description.

Naming Consistency5/5

The single name monitor_news_mentions follows a clean snake_case verb_noun pattern. With only one tool there is no risk of inconsistent conventions.

Tool Count3/5

One tool is thin for a server framed as a 'monitor': it can fetch mentions but offers no separate operations for managing saved queries or checking run history. The single-tool surface is workable but borderline for the stated scope.

Completeness3/5

The find-mentions operation is rich (multi-query, filtering, incremental state via state_key, error reporting), but there are no companion tools for listing prior runs, managing state keys, or configuring recurring alerts. Agents can work around this by passing state_key manually, but the lifecycle is notably partial.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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