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ai-memory vs Google Ads MCP Server: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of ai-memory and Google Ads MCP Server — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

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ai-memory

AkitaOnRails

Free

Open-source, self-hosted long-term memory server that lets Claude Code, Codex, Cursor and other agent CLIs share context and hand off sessions.

Key features

  • Cross-Agent Handoffs: Typed, claim-once handoffs let you continue a session in a different agent CLI.
  • Automatic Capture: Lifecycle hooks record prompts, tool calls and session boundaries, sanitized before storage.
  • Markdown Wiki Storage: Git-backed wiki of .md files is the source of truth, with a rebuildable derived database index.
  • Multi-Mode Search: Full-text, entity and graph search plus optional vector search.
  • Team Sharing: Per-project knowledge sharing with multi-user auth, per-person attribution and an audit log.
  • Self-Hosted Single Binary: Rust binary or Docker server running on your laptop, homelab or LAN host.
  • Zero-LLM Default: Capture and search work without an LLM API key.
  • Broad Agent Support: Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Kiro CLI, Grok Build CLI and more.

Best for

  • A developer starts a feature in Claude Code and continues it in Codex without re-explaining the project.
  • A team shares project knowledge across members' coding agents with an audit trail.
  • An engineer keeps memory on a homelab server so every laptop and agent sees the same context.
  • A privacy-focused user runs fully local memory capture with no LLM calls or cloud service.
  • A maintainer browses and edits agent memories as plain markdown in a git repository.
  • A developer onboards a new agent CLI to an existing project through MCP install commands.
View ai-memory details
Google Ads MCP Server logo

Google Ads MCP Server

HireOtto

Freemium

Hosted and open-source Model Context Protocol server to manage Google Ads from MCP clients (e.g., Claude) without Google Cloud or terminal setup.

Key features

  • Hosted Remote MCP Server: A hosted endpoint that lets MCP clients (for example Claude Desktop) call Google Ads management tools without requiring Google Cloud setup, terminal use, or manual JSON edits.
  • Self-Hosted Node.js/TypeScript Implementation: Open-source repositories provide Node.js and TypeScript servers you can run locally or in containers with simple .env-based configuration for Google Ads API credentials.
  • Campaign Management Tools: Programmatic creation, retrieval, update, and budget adjustments for campaigns and ad groups, including parameters like campaign_id and budget_euros for direct modification.
  • Unified MCP Tooling Endpoint: All tools are exposed via a consistent POST /api/mcp interface and support MCP streamable HTTP JSON-RPC (initialize, tools/list, tools/call) for agent interoperability.
  • Analytics & Reporting: Export campaign and performance reports to CSV or JSON, request date-range filtered metrics, and retrieve conversion and cost statistics for optimization and reporting.
  • Rate-Limit Handling & Retries: Built-in handling of Google Ads API rate limits with automatic retries to reduce manual error-handling and throttle issues.
  • OAuth2 & Simple Configuration: Supports standard Google Ads OAuth2 credentials (client id/secret, refresh token, developer token) with inline JSON in .env for straightforward setup.
  • Extensible Tools & Workflows: Modular tool implementations (accounts, campaigns, ads, keywords, conversions, performance, shopping) allowing customization and addition of new MCP tools.
  • MCP-compatible toolset exposing Google Ads operations (campaigns, ad groups, ads, keywords, conversions, performance, analytics, shopping).
  • POST /api/mcp endpoint with ToolResponse shape ({ok:true,data} | {ok:false,error}) and support for MCP Streamable HTTP JSON-RPC (initialize, tools/list, tools/call).
  • Campaign management operations (create/update budgets, retrieve campaign stats).
  • Analytics & reporting with export options to CSV or JSON (export_report with parameters: format, days).
  • Autocomplete/keyword-sourcing tools (Google Autocomplete, Trends, keyword clustering in some forks).
  • Configuration via .env (inline JSON or environment variables) and example .env templates included.
  • OAuth2 support: instructions and scripts to obtain refresh tokens; requires Google Ads developer token, client ID/secret, refresh token, optional login-customer-id.
  • Automatic handling of Google Ads API rate limits and retry logic.
  • Multiple installation options: npm/pnpm (local/global), npx (no install), Docker images available on GHCR.
  • Claude Desktop integration helpers and example claude_desktop_config.json for adding to MCP clients.

Best for

  • Agent-driven Campaign Launches: Use an MCP-capable agent (like Claude) to create and configure Google Ads campaigns via natural-language prompts without touching Google Cloud or API JSON.
  • Daily Campaign Monitoring and Alerts: Query campaign performance and get daily summaries or alerts from the MCP server for rapid status checks and anomaly detection.
  • Automated Budget Optimization: Programmatically adjust daily budgets and bids across accounts using scheduled agent workflows or triggered rules exposed through MCP tools.
  • Exporting Stakeholder Reports: Generate CSV or JSON exports of campaign, conversion, and cost metrics for sharing with teams or importing into BI tools.
  • Keyword & Trend Research Integration: Combine Google Autocomplete, Trends, and Search Console-derived keyword data (available in some implementations) to inform campaign targeting from the same MCP endpoint.
  • Local Development and Custom Extensions: Developers can run the open-source server locally, add custom tools (e.g., custom analytics or bidding strategies), and integrate with CI/CD or containerized deployments.
  • Integrating Ads Management into ChatOps: Embed Google Ads operations into chat-based workflows or agent orchestrations so non-technical marketers can request changes conversationally.
  • Manage Google Ads campaigns programmatically from an MCP-enabled chat assistant or desktop client (e.g., create/update campaigns, budgets).
  • Run daily campaign monitoring and automated campaign optimization workflows via LLM agents.
  • Export campaign reports for analysis (CSV/JSON) and feed results back into an agent for decision-making.
  • Perform keyword research and clustering by combining Google Autocomplete/Trends data with Search Console (in supported forks).
  • Integrate Google Ads controls into internal tooling without exposing direct Google Cloud or manual JSON configuration to non-technical users.
View Google Ads MCP Server details