# io.github.enzoemir1/adops-mcp (npm · adops-mcp-server)

CSV-first analytics for Google Ads + Meta Ads: reporting, anomaly detection, budget optimization.

- Trust score: 69/100 (medium)
- Change this week: +19
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `adops-mcp-server`: 69/100 (this document), [markdown](https://verifymcp.io/servers/enzoemir1-adops-mcp/adops-mcp-server.md), [page](https://verifymcp.io/servers/enzoemir1-adops-mcp/adops-mcp-server)

## Channel facts

- Registry: `npm`
- Package: `adops-mcp-server`
- Version: `1.3.0`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 87/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 99 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 81/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2873 tokens (~136/item across 21 items; 17 tools + 4 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 91/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 72% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add enzoemir1-adops-mcp -- npx -y adops-mcp-server
```

### Codex

```bash
codex mcp add enzoemir1-adops-mcp -- npx -y adops-mcp-server
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "enzoemir1-adops-mcp": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "adops-mcp-server"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add enzoemir1-adops-mcp --command npx --arg -y --arg adops-mcp-server
```

### Hermes

```yaml
mcp_servers:
  enzoemir1-adops-mcp:
    command: "npx"
    args: ["-y", "adops-mcp-server"]
```

### Other

```json
{
  "mcpServers": {
    "enzoemir1-adops-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "adops-mcp-server"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 69, +4)

- [functional improvement] Stability: unverified → 0.27

### 2026-08-02 (score 65, +29)

- [security regression] Provenance: unverified → fail
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Install scripts: unverified → pass
- [security improvement] Malware scan: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional improvement] License: unverified → pass
- [functional improvement] Maintenance: unverified → pass
- [functional] Licence: MIT

### 2026-08-01 (score 36, +31)

- [security] Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window).
- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] Schema quality: unverified → 100
- [functional improvement] MCP protocol: unverified → pass

### 2026-07-31 (score 5, −75)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-29 (score 80, +54)

- [security regression] Provenance: unverified → fail
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Install scripts: unverified → pass
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] Schema quality: unverified → 100
- [functional improvement] License: unverified → pass
- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] Schema quality: unverified → excellent
- [functional] Licence: MIT

### 2026-07-28 (score 26, −24)

- [functional regression] Schema quality: 100 → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional improvement] Dependency health: unverified → partial

### 2026-07-27 (score 50)

First indexed and scored.

## MCP tools (17)

### `ad_demo_seed` (~100 tokens)

Seed Demo Ad Portfolio

Create a realistic cross-platform ad portfolio for trying AdOps without real data. Seeds 2 connections (Google + Meta), 8 campaigns across performance tiers, 30 days of daily metrics (240 rows), and pre-computed anomaly alerts. Use this to explore what AdOps can do, then switch to `ad_csv_import` with your real Google/Meta exports for production use. Safe to call multiple times — appends a new portfolio each call.

### `ad_csv_import` (~377 tokens)

Import Ad Campaign Data from CSV

Import real campaign + performance data from a Google Ads or Meta Ads CSV export. After import, every AdOps tool (ads_report, budget_analyze, anomaly_detect, ab_test_analyze, competitor_benchmark, forecast_spend) operates on your real data. No API keys or OAuth required — just export from your ad dashboard and import here.

HOW TO EXPORT:
  • Google Ads: Reports → Campaign performance → Download → Comma-separated (.csv). Include "Day" segment for daily metrics.
  • Meta Ads Manager: Campaigns view → Export → Campaign performance (.csv). Check "Include breakdowns: By day" for daily metrics.

INPUT: Provide either csv_path (absolute path to file on disk) OR csv_content (raw CSV string). Optional connection_name groups imports; re-importing with the same name appends new data. Returns a summary with campaigns_imported, metrics_imported, warnings, and the connection id to reference in other tools.

Input parameters:

- `account_id` (string): Your platform account ID (e.g., Google customer ID "123-456-7890" or Meta "act_1234567890"). Used to identify the connection.
- `connection_name` (string): Friendly name for this data source (e.g., "ACME Google Account"). Defaults to "<Platform> Ads (CSV Import)".
- `csv_content` (string): Raw CSV content as a string. Use for small exports pasted directly into the tool.
- `csv_path` (string): Absolute path to a CSV file on disk. Preferred for large exports. Example: "/Users/you/Downloads/google_ads_report.csv"
- `platform` (string, required): Which platform the CSV is from: "google" (Google Ads Editor or web UI export) or "meta" (Meta Ads Manager → Export → Campaigns)

### `ads_export_recommendations` (~370 tokens)

Export Budget Recommendations (Bulk-Edit Ready)

Export budget_analyze recommendations as a Google Ads Editor CSV, Meta Ads Manager CSV, JSON, or Markdown — so you can take action in one bulk paste instead of editing campaigns one by one. AdOps does not call Google/Meta write APIs (that would require OAuth + developer token approval for every user); instead, this tool hands you the exact CSV rows those dashboards expect, and you paste them in. Perfect for the "pause anything with CPA over 50 and reallocate budget" workflow. Use platform=google with format=google_ads_csv (import into Google Ads Editor). Use platform=meta with format=meta_ads_csv (bulk edit in Meta Ads Manager Power Editor). Use json to pipe into n8n or your own automation. Use markdown for human review before acting. min_delta_pct filters out noise (e.g. 0.15 = only show changes >=15%).

Input parameters:

- `format` (string, required): Output format. google_ads_csv and meta_ads_csv produce platform-specific bulk-edit CSVs ready to paste into Google Ads Editor or Meta Ads Manager. json is for piping into automations (n8n, Zapier). m…
- `limit` (integer): Max recommendations to export. Default 50.
- `min_delta_pct` (number): Only include recommendations whose budget change is at least this fraction (e.g. 0.1 = 10%). Filters out minor adjustments.
- `optimization_goal` (string): Goal fed to budget_analyze.
- `output_path` (string): Absolute file path to write the output to. If omitted, returns the content inline in the tool response.
- `platform` (string): Restrict to a single platform. Required for google_ads_csv or meta_ads_csv to avoid mixed output.

### `platform_connect` (~148 tokens)

Connect Ad Platform

Register a Google Ads or Meta Ads account in the AdOps workspace so subsequent tools (campaign_list, campaign_create, ads_report) can target it. Input: platform ("google_ads"|"meta_ads"), name (display label), account_id (the external ad account id). Returns the stored connection object with a generated UUID and status="active". Safe to call with the same platform+account_id — returns the existing connection instead of erroring (idempotent).

Input parameters:

- `account_id` (string, required): Platform-specific ad account ID (Google: 10 digits, Meta: act_XXXXXXXXX)
- `name` (string, required): Friendly connection name
- `platform` (string, required): Ad platform to connect

### `campaign_list` (~171 tokens)

List Campaigns

Unified listing of campaigns across Google Ads and Meta Ads in a single view. Optional filters: platform ("google_ads"|"meta_ads"), status ("draft"|"active"|"paused"|"ended"|"archived"), query (free-text over campaign name). Pagination via limit (default 20, max 100) and offset. Returns {total, showing, offset, campaigns[]} where each campaign summary includes id, name, platform, status, objective, daily_budget, currency, and start_date. Use the returned id with campaign_update, campaign_pause_resume, or ab_test_analyze.

Input parameters:

- `limit` (integer)
- `offset` (integer)
- `platform` (string): Filter by platform
- `query` (string): Search campaign names
- `status` (string): Filter by status

### `campaign_create` (~185 tokens)

Create Campaign

Create a new ad campaign in the AdOps workspace. Accepts unified parameters (platform, name, objective, bidding_strategy, daily_budget, currency, start_date, end_date, targeting) and stores a canonical UnifiedCampaign record with status="draft". Returns the created campaign summary plus next_steps guidance. Requires an active platform connection (see platform_connect) — will auto-associate the first active connection for the chosen platform.

Input parameters:

- `bidding_strategy` (string): Bidding strategy
- `currency` (string): ISO 4217 currency
- `daily_budget` (number, required): Daily budget
- `end_date` (string): ISO date (optional)
- `name` (string, required): Campaign name
- `objective` (string, required): Campaign objective
- `platform` (string, required): Target platform
- `start_date` (string): ISO date (defaults to today)
- `targeting` (object)

### `campaign_update` (~133 tokens)

Update Campaign

Patch-update an existing campaign. Pass campaign_id (UUID from campaign_list or campaign_create) plus any subset of: name, status, daily_budget, bidding_strategy, end_date. Fields you omit are left unchanged. Returns {message, updated_fields[], campaign} or an error if campaign_id is not found. Prefer campaign_pause_resume for batch status changes.

Input parameters:

- `bidding_strategy` (string)
- `campaign_id` (string, required): AdOps campaign ID
- `daily_budget` (number)
- `end_date`
- `name` (string)
- `status` (string)
- `total_budget`

### `campaign_pause_resume` (~108 tokens)

Pause or Resume Campaigns

Batch-change the status of up to 50 campaigns in one call. action="pause" sets status to "paused"; action="resume" sets status to "active". Missing campaign_ids are reported in the errors array but do not fail the whole batch. Returns {action, updated, failed, campaigns[], errors?}. Use this for emergency pause during an incident or weekend shutoff.

Input parameters:

- `action` (string, required): Action to perform
- `campaign_ids` (array, required): Campaign IDs to toggle

### `ads_report` (~183 tokens)

Cross-Platform Performance Report

Aggregate performance metrics across Google Ads and Meta Ads into a single unified view. Input: date_range ({start, end} as YYYY-MM-DD, defaults to the last 7 days), optional platform filter, optional campaign_ids filter, optional sort_by ("spend"|"roas"|"conversions"|"ctr"|"cpc"), and limit. Returns {period, totals (spend, impressions, clicks, conversions, revenue, ROAS, CPC, CTR), by_platform, campaigns[] (sorted per sort_by), top_performers, underperformers}. This is the entry point for most analysis workflows.

Input parameters:

- `campaign_ids` (array): Filter specific campaigns
- `date_range` (object): Defaults to last 7 days
- `limit` (integer)
- `platform` (string): Filter by platform
- `sort_by` (string)

### `budget_analyze` (~114 tokens)

Budget Analysis & Optimization

Analyze how the current ad budget is distributed and produce actionable reallocation recommendations. Input: optimization_goal ("maximize_roas"|"maximize_conversions"|"minimize_cpa") and optional platform filter. Returns {goal, current_allocation (by platform + campaign), recommendations[] (each with campaign_id, current_budget, suggested_budget, rationale, expected_impact), projected_lift}. Pair with budget_reallocate to execute the recommendations.

Input parameters:

- `optimization_goal` (string)
- `platform` (string): Filter by platform

### `budget_reallocate` (~134 tokens)

Reallocate Budget

Transfer a dollar amount from one campaign's daily budget to another. Works across platforms (e.g. shift $50/day from a Google Ads search campaign to a Meta Ads retargeting campaign). Input: from_campaign_id, to_campaign_id (UUIDs, must differ), amount (positive number in campaign currency). Rejects the call if from_campaign_id === to_campaign_id or if the source campaign would go below zero. Returns the updated budgets for both campaigns.

Input parameters:

- `amount` (number, required): Amount to transfer
- `from_campaign_id` (string, required): Source campaign
- `to_campaign_id` (string, required): Destination campaign

### `audience_insights` (~102 tokens)

Audience Insights

Demographic and behavioural breakdown of the audiences served by your ads. Input: platform (optional — omit for all platforms) and optional campaign_id to scope to a single campaign. Returns {age_distribution, gender_distribution, top_geos, top_interests, device_breakdown, total_impressions, engagement_rate}. Use when refining targeting or reporting audience coverage.

Input parameters:

- `campaign_id` (string): Specific campaign (optional)
- `platform` (string, required): Platform to analyze

### `creative_specs` (~114 tokens)

Creative Specifications

Platform-specific creative requirements for ad formats. Returns the exact image dimensions, aspect ratios, video duration and codec, headline/primary-text character limits, supported CTA buttons, and file size ceilings for each ad format. Input: platform ("google_ads"|"meta_ads") and optional format filter (e.g. "responsive_display", "video", "carousel", "single_image"). Use this before building creatives to avoid rejection at upload time.

Input parameters:

- `format` (string)
- `platform` (string, required): Platform to get specs for

### `anomaly_detect` (~148 tokens)

Detect Performance Anomalies

Scan campaigns for statistical anomalies vs. a rolling baseline. Flags CPC spikes, CTR drops, sudden spend surges, and conversion cliffs. Input: sensitivity ("low"|"medium"|"high" — controls the z-score threshold), lookback_days (baseline window, default 14), optional platform filter. Returns {anomalies_found, severity_breakdown (critical|high|medium|low counts), alerts[] (each with campaign_id, metric, baseline, current, deviation, severity, reason)}. Run daily to catch issues before they burn budget.

Input parameters:

- `lookback_days` (integer)
- `platform` (string): Filter by platform
- `sensitivity` (string): Detection sensitivity

### `ab_test_analyze` (~160 tokens)

A/B Test Analysis

Compare two campaigns as A/B test variants and determine statistical significance. Input: campaign_id_a, campaign_id_b, primary_metric ("ctr"|"conversion_rate"|"roas"|"cpc"|"cpa"). Runs a two-proportion z-test (or means comparison for continuous metrics), computes p-value and 95% confidence interval, identifies the winner, and returns {winner, confidence_level, p_value, lift_percent, sample_size_a, sample_size_b, significant (bool), recommendation}. Use with lift ≥5% and p<0.05 as a decision rule.

Input parameters:

- `campaign_id_a` (string, required): Variant A campaign
- `campaign_id_b` (string, required): Variant B campaign
- `primary_metric` (string)

### `competitor_benchmark` (~142 tokens)

Industry Benchmark Comparison

Compare your ad performance against industry averages for a chosen vertical. Input: industry (e.g. "ecommerce", "saas", "finance", "healthcare", "education", "travel", "real_estate", "legal"), optional platform filter. Returns {industry, your_metrics, benchmarks (CTR, CPC, CPM, conversion_rate, CPA, ROAS industry averages), comparison (percent above/below benchmark per metric), recommendations[]}. Benchmarks are curated static tables — not live market data.

Input parameters:

- `industry` (string, required): Industry for benchmarking (e.g., "ecommerce", "saas", "finance")
- `platform` (string)

### `forecast_spend` (~124 tokens)

Spend & Performance Forecast

Project future ad spend and performance based on recent historical trends. Input: period_days ("7"|"14"|"30") and optional platform filter. Uses moving-average extrapolation of spend, impressions, clicks, conversions, and revenue across the last 14 days. Returns {period_days, platform, projected (spend, impressions, clicks, conversions, revenue, ROAS, CPC, CTR), confidence_level, warnings[]}. Confidence drops when recent data is volatile or campaigns were paused.

Input parameters:

- `period_days` (string): Forecast period
- `platform` (string)

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/enzoemir1-adops-mcp/adops-mcp-server#diagnostics

## Score history

- 2026-08-03: 69
- 2026-08-02: 65
- 2026-08-01: 36
- 2026-07-31: 5
- 2026-07-30: 80
- 2026-07-29: 80
- 2026-07-28: 26
- 2026-07-27: 50

## Links

- npm package: https://www.npmjs.com/package/adops-mcp-server
- Socket report: https://socket.dev/npm/package/adops-mcp-server
- Repository: https://github.com/enzoemir1/adops-mcp
- Changelog RSS feed: https://verifymcp.io/servers/enzoemir1-adops-mcp/adops-mcp-server/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/enzoemir1-adops-mcp/adops-mcp-server/changelog.json
- HTML version of this page: https://verifymcp.io/servers/enzoemir1-adops-mcp/adops-mcp-server