
Google Ads Intent Mcp
io.github.davidmosiah由 GitHub 帳號 davidmosiah 發布v0.1.1更新於 Oct 24, 2026
Dry-run-first Google Ads search-term intent analyzer and negative-keyword MCP for agents.
概覽
一個本機 MCP 伺服器,從 CSV 匯出檔分類 Google Ads 搜尋字詞意圖,並產生僅演練的否定關鍵字方案,保護購買意圖查詢。
- 可以做什麼
- 將單一搜尋字詞或整批匯出的搜尋字詞分類為浪費、購買、研究或競品意圖;可選的 LLM 精煉路徑預設關閉,未設定後端時會退回啟發式分類器。在本機分析匯出的搜尋字詞 CSV,接受搜尋字詞、費用、點擊、轉換與曝光等常見欄位名稱。產生否定關鍵字方案但不實際套用,並保護購買與轉換字詞不被排除。在使用操作類工具前,回報安裝與執行指引、無需憑證的 CSV/API 就緒狀態,以及演練、帳戶與匯出界線。
- 適用情境
- 當助理需要檢視 Google Ads 搜尋字詞匯出檔以找出浪費支出並建議否定關鍵字,同時避免誤排除有轉換的查詢時使用。適合以演練為先、不希望更動線上帳戶的清理流程。
- 執行需求
- 需要本機 Python 執行環境;安裝 PyPI 套件 google-ads-intent-mcp,使用 MCP 時需加 mcp 附加項目。CSV 分析無需憑證。即時 Google Ads 存取為選用,使用 GOOGLE_ADS_CLIENT_ID、GOOGLE_ADS_CLIENT_SECRET、GOOGLE_ADS_DEVELOPER_TOKEN、GOOGLE_ADS_REFRESH_TOKEN 與 GOOGLE_ADS_CUSTOMER_ID。可選的 LLM 精煉需要 OPENAI_API_KEY 與 openai 套件。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Google Ads Intent Mcp,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
Google Ads Intent MCP
Dry-run-first Google Ads search-term intent analyzer + negative-keyword MCP for agents.
Look at search terms safely before spending budget.
[npm version] [npm downloads] [License MIT] [Built for MCP]
[GitHub stars] [CI status] [Part of the Delx agent stack] [Category]
mcp-name: io.github.davidmosiah/google-ads-intent-mcp
⭐ If this agent-first tool helps your workflow, please star the repo. Stars make this tooling easier for other builders to discover and help Delx keep shipping open infrastructure.
🧱 Part of the Delx agent stack — 15 open-source MCP servers across body, reach and coordination.
Dry-run-first Google Ads search-term intent analyzer for agents. It helps Codex, Claude, Cursor, Hermes, OpenClaw and other MCP clients classify search terms, protect buyer intent and draft negative-keyword plans from CSV exports before any live account change.
Use it when an agent needs to reduce wasted spend without accidentally excluding buyer-intent queries.
HTTP (v2 stateless)
Not shipped. Optional FastMCP extra is stdio-only (same skip as delx-agent-utilities: no /health without extra Starlette routes). Dry-run analysis stays request-stateless over stdio.
Why It Exists
Google Ads cleanup is risky when agents act directly on accounts. This package makes the safe path the default:
- analyze exported search-term CSVs locally
- classify waste, buyer, research and competitor intent
- draft negative-keyword plans without applying them
- expose
manifest,connection_statusandprivacy_auditbefore action tools - keep live mutation out of v0.1
Install
With MCP support:
Published on PyPI: google-ads-intent-mcp. Release automation uses PyPI Trusted Publishing, so GitHub Actions can publish future versions without long-lived PyPI tokens. See docs/pypi-publishing.md.
CLI
Intent classification
The classifier is a deterministic, dependency-free heuristic with broad,
cross-vertical signal coverage (ecommerce, B2B/SaaS, local services, health,
finance, education and more) — not just gaming traffic. It sorts each search
term into waste, buyer, research or competitor intent and protects
converting queries from being flagged as negatives.
An optional LLM/embeddings-backed refinement path is available and is off by default. It requires no extra dependencies or API keys for normal use, and always falls back to the heuristic when no backend is configured:
To actually call a backend, set OPENAI_API_KEY (and optionally
GOOGLE_ADS_INTENT_LLM_MODEL, default gpt-4o-mini) and install the openai
package. Without those, --llm is a no-op that keeps the heuristic result.
Each classification reports which path produced it via a source
(heuristic or llm) field.
MCP
Hermes-style config:
Recommended first calls:
google_ads_connection_statusgoogle_ads_privacy_auditgoogle_ads_analyze_search_termsgoogle_ads_build_negative_plan
Agent Surfaces
Copy-Paste Agent Prompt
CSV Format
The parser accepts common exported columns such as:
search_term,Search term,Querycost,Cost,cost_microsclicks,Clicksconversions,Conversions,Conv.impressions,Impr.,Impressions
Safety Model
- CSV analysis is local.
- Negative plans are dry-run only.
- Buyer/conversion terms are protected from automatic exclusion.
- OAuth tokens, developer tokens and account identifiers should stay in local environment/config files.
Development
📧 Contact & Support
- 📨 [email protected] — general questions, integration help, partnerships
- 🐛 Bug reports / feature requests — GitHub Issues
- 🐦 Updates — @delx369 on X
- 🌐 Site — wellness.delx.ai
來源:README.md,提交 30098d3
工具
0版本歷史
1- v0.1.1最新Sep 16, 2026
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