--- title: "Tagtoo Agency Harness | The AI Toolkit for Digital Agencies" description: "An open-source data standardization & context layer by Tagtoo: unified data collection, AI-ready cleaning, and standardized output, so marketing teams and AI agents work from one consistent foundation." lang: en canonical: https://www.adchief.ai/en/ html_url: https://www.adchief.ai/en/ content_signal: "ai-train=yes, search=yes, ai-input=yes" tokens_est: 2203 --- # Tagtoo Agency Harness The AI Toolkit for Digital Agencies The data standardization & context layer for agencies and brands adopting AI Harness consolidates your organization’s scattered AI workflows and fragmented data, cleaning everything into one unified, AI-ready format — so marketing teams and AI agents can produce reliable work from the same foundation. - [Contact us](mailto:tagtoo@tagtoo.org) - [Explore the six workflows](#workflows) ## The data governance gap of the AI era When agencies and brands race to adopt AI, the first wall they hit isn’t “the model isn’t smart enough” — it’s that their data simply isn’t ready to be fed to AI. It shows up as three systemic pain points: ### Chaotic internal AI workflows Every team and every operator uses different prompts, different tools, and different steps to do the same job — reports, competitive research, KOL discovery. With no standard process, results can’t be reproduced, new hires can’t ramp up, and knowledge never accumulates. ### Fragmented, inconsistent data Ad performance lives across Meta, Google, and GA4; social buzz across Threads, Instagram, and Facebook; competitor and KOL data is scattered across even more sources. Fields, units, naming, and collection methods all differ — isolated data silos that must be manually stitched together and cleaned for every single analysis. ### Inconsistent deliverables Same company, same client — yet this month’s report looks one way and next month’s looks another. One person’s deck never matches a colleague’s layout, colors, fonts, or metric definitions. It looks unprofessional externally and is incomparable internally. These gaps are the root cause of why AI can’t truly be put to work: feed AI messy, fragmented, inconsistently formatted data and its output will be unstable, untrustworthy, or simply wrong. Tagtoo Agency Harness was open-sourced to fix this at the source. ## Unified collection → AI-ready cleaning → Standardized output The core value of the toolkit is fully standardizing the two most error-prone, least standardized stages: data collection and data cleaning. ### STEP 1: Unified data collection Meta / Google / GA4 ad performance; Threads / IG / Facebook / TikTok / YouTube social and creator data; competitor ads; retail panels; DXP audiences — every source is fetched through Tagtoo-hosted MCP connectors via one single, consistent interface. No more ad-hoc scrapers, per-person API keys, or hand-rolled 403 and pagination handling. ### STEP 2: Cleaned into AI-ready formats Raw data flows through each workflow’s analysis recipes to be cleaned, deduplicated, and aligned on fields and units, producing structured intermediate formats AI can consume directly: unified analysis JSON, share-of-voice tables, KOL / competitor schemas. Your AI agents receive clean, consistent, trustworthy context — not messy raw responses. ### STEP 3: Standardized output Every deck is rendered through a single brand theme, tagtoo_pptx.js (six byte-identical copies): covers, tables of contents, sections, colors, logos, and fonts are perfectly consistent. A built-in content-aware font selector (Arial for English, PingFang TC for Chinese) eliminates overlapping glyphs and mojibake in mixed-script text. ### Design philosophy: breadth of coverage Not depth in a single feature, but consolidating the daily workflows agencies repeat every day into an out-of-the-box, best-practice-sharing library of standardized skills. New hires are productive immediately, engineers keep full customization flexibility — and the industry finally stops reinventing the wheel every single day. ## Six standardized workflows Each workflow is a self-contained module, shipped with a README.md for humans, an AGENTS.md for AI agents, and a conversational SOP instruction.md. ### Ad performance reporting `ad-report` Reads your clients’ Meta Ads, Google Ads, and GA4 CSVs to auto-generate monthly / quarterly / weekly PPTX reports and daily ad health-check HTML reports — and answers ad questions on the spot. Supports 20+ CSV data types, 10 analysis dimensions, and full KPI computation (ROAS / CPA / CTR / VTR) with period-over-period analysis. ### Brand voice analysis `brand-voice-analysis` Collects third-party brand mentions across Threads and Instagram, analyzing sentiment, share of voice, and topics. Dual-signal collection (name + hashtag) with deduplication, comparing 1–4 brands at once (your client plus competitors). ### Competitive ad creative analysis `competitive-analysis` Gathers competitors’ ad placements, creative themes, slots, and social engagement into a “your brand vs. competitors vs. category leader” comparison deck, covering estimated Google (Search / GDN) performance, with the required ad-creative screenshot pages. ### Retail sales competitive analysis `retail-analysis` Uses Tagtoo retail panel data to benchmark your client’s product and category sales performance against the market. Retail panel data is proprietary, paid data — request access separately. ### DXP audience picker `dxp` A visual HTML interface (Chinese & English) that lets non-technical users browse and filter Tagtoo DXP audience segments (first-party / second-party / retail partner on-site), then trigger downstream audience reports. The audience catalog UI is deliberately open source; audience membership and activation data are proprietary. ### KOL recommendation `kol-recommendation` Searches Instagram and Threads by industry (10 categories) for influencers with sponsored-content experience, detecting sponsorship signals in Chinese and English, ranking by follower count, and producing a recommendation deck per KOL with profile photo, post screenshots, and a “why they fit” rationale. ### Server-side image pipeline All social images — profile photos, post thumbnails, ad creatives — are fetched server-side through the connector’s download_media MCP tool and returned as base64, bypassing the 403 blocks that IG / Facebook CDNs impose on client-side fetching, so screenshots embed reliably in every deck. ## Three-layer architecture Clear responsibilities and one-way dependencies: Customization → Skills → Tools. Lower layers know nothing about upper layers, fully decoupling workflow logic from data sources — the architectural foundation of “collect data once, reuse it across every client.” ### Customization layer — per-client Each client’s dedicated configuration and prompts (not in the public repository). ### Skills layer — this open-source repo Cross-client workflows, analysis recipes, and report templates shared across clients. ### Tools layer — private MCP servers Private servers wrapped in the MCP protocol: the Tagtoo Social API Connector, gcs-mcp, and Google Drive MCP. ## An open-core business model All workflow logic, analysis recipes, and report templates are open-sourced under Apache 2.0 — to gain market visibility and let practitioners across Asia easily read, adapt, and contribute. Tagtoo’s real moat is its data and hosted infrastructure: the repository contains no API keys and no free local scraping layer. Running any workflow end-to-end requires access to the Tagtoo Social API Connector (request it from Tagtoo) or standing up an equivalent MCP yourself. Some data sources — the retail panel and advanced DXP segments — are proprietary, paid data. ### Tagtoo Social API Connector The core MCP server of the tools layer, deployed on Google Cloud Run. It wraps upstream social data APIs with keys stored server-side, exposing Facebook, Instagram, Threads, TikTok, and YouTube creator queries, plus server-side image fetching via download_media and an /img image proxy endpoint. ## About Tagtoo & the community The toolkit is maintained by Tagtoo Limited, a Taiwanese company built on digital advertising technology and audience data (DXP), spanning programmatic ad buying and retail / e-commerce data analytics. Contributions via PRs, issues, and discussions are welcome. ### We welcome - Industry configuration templates - Cross-client skills & workflows - Domain knowledge - Non-moat scripts - Documentation - Bug fixes ### We don’t accept - Individual client configurations - Private client data - Hosted MCP server implementations - Logic bound to Tagtoo proprietary data ### What’s next: AI manager / AI consultant As the framework matures, Tagtoo plans “AI manager / AI consultant” services to help clients without engineering resources build and maintain customized versions within the framework — deepening partnerships, with part of the accumulated know-how flowing back into the open-source skills layer. ## Partnerships & adoption For business partnerships, connector access, pricing, and adoption consulting, get in touch. [Email tagtoo@tagtoo.org](mailto:tagtoo@tagtoo.org)