{"id":5499,"date":"2026-05-27T16:02:37","date_gmt":"2026-05-27T16:02:37","guid":{"rendered":"https:\/\/www.coffee.ai\/articles\/automate-monday-crm-bulk-import\/"},"modified":"2026-05-27T16:02:37","modified_gmt":"2026-05-27T16:02:37","slug":"automate-monday-crm-bulk-import","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/automate-monday-crm-bulk-import\/","title":{"rendered":"How to Automate monday.com CRM Bulk Contact Imports"},"content":{"rendered":"<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Weekly CSV uploads into monday.com often push duplicate records above 15% and leave activity data unlogged, so manual imports become an ongoing operational tax.<\/li>\n<li>A six-stage automation sequence with data prep, native mapping, Make and Zapier workflows, and Python API scripts can cut duplicate rates below 3% and remove recurring manual uploads.<\/li>\n<li>Pre-import quality gates that enforce 90% field completeness and bounce rates below 5% keep bad data out of the CRM.<\/li>\n<li>Zapier, Make, and custom API scripts each require setup time, ongoing monitoring, and carry scalability limits tied to task caps or rate limits.<\/li>\n<li>Teams ready to remove the maintenance cycle entirely can <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">explore Coffee\u2019s autonomous agent<\/a> and replace imports with real-time contact and activity capture.<\/li>\n<\/ul>\n<h2>Why Weekly CSV Uploads Have Become an Unsustainable Operational Tax<\/h2>\n<p>Sales professionals spend <a href=\"https:\/\/businessnewsdaily.com\/10469-business-technology-trends.html\" target=\"_blank\" rel=\"noindex nofollow\">nearly a quarter of their working day on manual tasks like inputting data<\/a>, and a significant portion of seller interactions never reach the CRM. The downstream cost is measurable: <a href=\"https:\/\/firecrawl.dev\/blog\/complete-guide-to-data-enrichment\" target=\"_blank\" rel=\"noindex nofollow\">Gartner estimates poor data quality costs organizations an average of $12.9 million annually<\/a>. <a href=\"https:\/\/aijourn.com\/validity-releases-state-of-crm-data-management-in-2025-report-revealing-disconnect-between-data-quality-and-ai-implementation\/\" target=\"_blank\" rel=\"noindex nofollow\">Validity\u2019s 2025 State of CRM Data Management report found that 76% of CRM users say less than half of their organization\u2019s data is accurate and complete<\/a>, and repeated CSV uploads compound that failure rate. Salesforce positions CSV imports to support both one-time migrations and recurring imports, with features like saving and reusing field mappings for ongoing efficiency. For teams still running weekly uploads, the import process has become the bottleneck. The six-stage automation sequence below removes that bottleneck by replacing manual uploads with structured data prep, reusable mapping templates, and workflow automation.<\/p>\n<h2>Quick Automation Setup: 6 Steps to Stop Manual Imports<\/h2>\n<ol>\n<li>Audit and clean source data before any import runs, targeting a duplicate rate below 3%.<\/li>\n<li>Build a standardized monday.com column-mapping template and lock field formats.<\/li>\n<li>Configure a Make scenario that triggers on new rows in Google Sheets and pushes contacts automatically.<\/li>\n<li>Add a Zapier multi-step workflow with a duplicate-check filter before any record is created.<\/li>\n<li>Deploy a Python script with error handling and rate-limit logic for high-volume or scheduled API imports.<\/li>\n<li>Establish a monitoring checklist with weekly duplicate-rate and field-completeness checks.<\/li>\n<\/ol>\n<h2>Stage 1: Data-Prep Checklist<\/h2>\n<p><strong>Inputs required:<\/strong> Source file (CSV or Excel), field mapping document, deduplication key (primary email address).<\/p>\n<ol>\n<li>Export a full snapshot of the source system and freeze it before editing.<\/li>\n<li>Define the canonical record with alignment across sales, marketing, and ops.<\/li>\n<li>Remove junk rows such as test accounts, internal addresses, and placeholder entries.<\/li>\n<li>Normalize formatting by standardizing country values (free-text fields commonly produce \u201cUnited States,\u201d \u201cUSA,\u201d and \u201cU.S.\u201d in the same column), phone numbers (+1 555-555-1234), and date formats.<\/li>\n<li>Deduplicate using exact email match first, then fuzzy company-plus-name matching with survivorship rules.<\/li>\n<li>Enrich gaps such as job title, company name, and LinkedIn URL before import.<\/li>\n<\/ol>\n<p><strong>Decision point:<\/strong> <a href=\"https:\/\/apollo.io\/insights\/how-to-clean-up-a-messy-crm-before-importing-it-into-a-new-system\" target=\"_blank\" rel=\"noindex nofollow\">A pre-import quality gate protects your CRM from inheriting source-system problems.<\/a> It requires duplicate rate below 3% to prevent record fragmentation, field completeness above 90% on required fields to keep contact data usable, and hard bounce rate below 5% to avoid deliverability penalties. Files that fail any of these thresholds should not proceed, because fixing data quality after import costs far more than blocking bad data at the gate.<\/p>\n<p><strong>Pitfall:<\/strong> Importing files with invalid emails transfers the data quality problem into your CRM instead of resolving it.<\/p>\n<p><strong>Ownership:<\/strong> RevOps or data ops, not individual sales reps.<\/p>\n<h2>Stage 2: Native monday.com Bulk Import Mapping Template<\/h2>\n<ol>\n<li>Open the target monday.com board and navigate to the Import Data option under the board menu.<\/li>\n<li>Upload the cleaned CSV or Excel file so monday.com\u2019s data importer can display the column-preview screen.<\/li>\n<li>Map each source column to the corresponding monday.com column type: Text to Text Column, Email to Email Column, Phone to Phone Column, and Status values to a Status Column with matching labels.<\/li>\n<li>Set the \u201cName\u201d column as the primary item name.<\/li>\n<li>Enable the \u201cUpdate existing items\u201d toggle and select email as the unique identifier to prevent duplicate item creation.<\/li>\n<li>Run a test import with 25 rows, verify field population and status labels, then proceed with the full file.<\/li>\n<\/ol>\n<p><strong>Pitfall:<\/strong> Mismatched status labels between the source file and the board\u2019s status column create blank or error values. Pre-populate the board\u2019s status column labels before importing.<\/p>\n<h2>Stage 3: Make Scenario Triggered from Google Sheets<\/h2>\n<p><strong>Inputs:<\/strong> Google Sheets spreadsheet with a defined header row, monday.com board ID, Make account.<\/p>\n<ol>\n<li>In Make, create a new scenario and add the Google Sheets module \u201cWatch New Rows.\u201d<\/li>\n<li>Connect your Google account and select the target spreadsheet and sheet tab.<\/li>\n<li>Add a monday.com module, \u201cCreate an Item,\u201d and map each Google Sheets column to the corresponding monday.com column ID.<\/li>\n<li>Add a Filter between the two modules so the flow only continues if the Email field is not empty and does not match an existing board item, using a monday.com \u201cSearch Items\u201d module upstream to check.<\/li>\n<li>Set the scenario schedule to run every 15 minutes or trigger on row addition through webhook.<\/li>\n<li>Activate the scenario and monitor the execution log for mapping errors on the first 50 runs.<\/li>\n<\/ol>\n<p><strong>Pitfall:<\/strong> Integration errors after campaign lead uploads commonly distort lead-source reporting. Add a \u201cSource\u201d column mapped to a fixed value such as \u201cGoogle Sheets Sync\u201d to preserve attribution.<\/p>\n<h2>Stage 4: Zapier Multi-Step Duplicate-Check Workflow<\/h2>\n<ol>\n<li>Create a new Zap and set the Trigger to Google Sheets \u2014 \u201cNew Spreadsheet Row.\u201d<\/li>\n<li>Add Step 2 as monday.com \u2014 \u201cSearch Items by Column Value,\u201d and search the Email column for the incoming email address.<\/li>\n<li>Add Step 3 as a Zapier Filter, \u201cOnly continue if,\u201d so the Zap runs only when the search returns zero results.<\/li>\n<li>Add Step 4 as monday.com \u2014 \u201cCreate Item,\u201d and map all required fields from the trigger row.<\/li>\n<li>Add Step 5 as an optional Slack notification confirming that the new contact was created, including the item name and board.<\/li>\n<\/ol>\n<p><strong>Pitfall:<\/strong> Duplicates arise when import processes fail to match existing emails. The search-before-create pattern in Steps 2 and 3 serves as the primary guard against this failure mode.<\/p>\n<h2>Stage 5: API Python Example with Error Handling and Rate-Limit Logic<\/h2>\n<pre>import requests, time, csv API_KEY = \"your_monday_api_key\" BOARD_ID = \"your_board_id\" ENDPOINT = \"https:\/\/api.monday.com\/v2\" HEADERS = {\"Authorization\": API_KEY, \"Content-Type\": \"application\/json\"} def create_item(name, email, company): query = f''' mutation {{ create_item ( board_id: {BOARD_ID}, item_name: \"{name}\", column_values: \"{{\"email\":{{\"email\":\"{email}\",\"text\":\"{email}\"}},\"text\":\"{company}\"}}\" ) {{ id }} }} ''' response = requests.post(ENDPOINT, json={\"query\": query}, headers=HEADERS) if response.status_code == 429: time.sleep(60) return create_item(name, email, company) response.raise_for_status() return response.json() with open(\"contacts.csv\", newline=\"\") as f: reader = csv.DictReader(f) for i, row in enumerate(reader): try: create_item(row[\"Name\"], row[\"Email\"], row[\"Company\"]) time.sleep(0.5) # respect rate limits except Exception as e: print(f\"Row {i} failed: {e}\")<\/pre>\n<p><strong>Pitfall:<\/strong> monday.com\u2019s API enforces minute rate limits of 1,000\u20135,000 queries per minute depending on plan, with some endpoints such as app subscriptions queries limited to 120 per minute. The <code>time.sleep(0.5)<\/code> call and the 429-retry block help manage this. Log all failures to a separate error CSV for manual review. Once your automation is live, whether through Make, Zapier, or a custom API script, the work shifts from setup to ongoing monitoring and quality assurance.<\/p>\n<h2>Stage 6: Ongoing Maintenance and Monitoring Checklist<\/h2>\n<ol>\n<li>Run a weekly duplicate scan using monday.com\u2019s built-in duplicate detection or a Make or Zapier deduplication scenario.<\/li>\n<li>Check field completeness monthly, targeting email validity above 95% and required fields such as name, company, and owner above 90%.<\/li>\n<li>Set 90-day decay alerts for contacts with no logged activity.<\/li>\n<li>Audit ownership monthly to identify orphaned contacts with no assigned rep.<\/li>\n<li>Review import error logs after every automated run and resolve failures within 48 hours.<\/li>\n<li>Refresh enrichment data every 3\u20136 months, because 30\u201340% of B2B CRM contact data goes stale every year.<\/li>\n<\/ol>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">See how Coffee eliminates ongoing maintenance<\/a> by replacing recurring imports with continuous, agent-driven updates.<\/p>\n<h2>Zapier vs Make vs API: Setup Time, Cost, Duplicate Handling, and Scalability<\/h2>\n<p>Each automation approach carries distinct trade-offs in setup effort, ongoing cost, and scalability. The comparison below highlights how Zapier, Make, and a Python API script differ so you can match the option to your team\u2019s technical skills and contact volume.<\/p>\n<table>\n<thead>\n<tr>\n<th>Tool<\/th>\n<th>Setup Time<\/th>\n<th>Monthly Cost (entry tier)<\/th>\n<th>Duplicate Handling<\/th>\n<th>Scalability Limits<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Zapier<\/td>\n<td>1\u20132 hours<\/td>\n<td>~$20\u2013$50 (task-based)<\/td>\n<td>Search-before-create filter, manual configuration required<\/td>\n<td>Task caps per plan, multi-step Zaps consume tasks quickly at volume<\/td>\n<\/tr>\n<tr>\n<td>Make<\/td>\n<td>2\u20134 hours<\/td>\n<td>~$9\u2013$29 (operation-based)<\/td>\n<td>Search module plus filter, more flexible routing than Zapier<\/td>\n<td>Operation caps, complex branching increases operation consumption<\/td>\n<\/tr>\n<tr>\n<td>Python API<\/td>\n<td>4\u20138 hours<\/td>\n<td>Infrastructure cost only (hosting and compute)<\/td>\n<td>Custom logic with exact and fuzzy matching possible<\/td>\n<td>API rate limits (see Stage 5), and ongoing engineering maintenance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>When Bulk Imports Become Unsustainable<\/h2>\n<p>Three measurable signals show that the automation recipes above have reached their ceiling.<\/p>\n<ul>\n<li><strong>Weekly import hours exceed 4.<\/strong> <a href=\"https:\/\/nimble.com\/blog\/crm-for-small-business\" target=\"_blank\" rel=\"noindex nofollow\">Teams spending more time exporting CSV files and manually updating contact records than working relationships have reached the point where scripted imports are unsustainable.<\/a><\/li>\n<li><strong>Duplicate rate exceeds 15%.<\/strong> <a href=\"https:\/\/kumo.ai\/resources\/learn\/entity-resolution\/\" target=\"_blank\" rel=\"noindex nofollow\">Enterprise databases show an average 15\u201325% duplicate data rate.<\/a> A rate above 15% in a mid-size board signals that deduplication logic is failing faster than it can be corrected.<\/li>\n<li><strong>Activity data is missing.<\/strong> A significant portion of seller interactions never reaches the CRM, and no CSV import process can retroactively capture email threads, call transcripts, or calendar events.<\/li>\n<\/ul>\n<p>The $12.9 million annual cost cited earlier becomes concrete when it shows up as wasted rep time, missed follow-ups, or duplicate outreach. When any two of the three signals above appear together, those abstract costs turn into measurable operational drag, and the import process generates more cost than it prevents.<\/p>\n<h2>How an Autonomous Agent Replaces monday.com Bulk Imports<\/h2>\n<p>The most complete way to automate monday.com CRM bulk contact imports is to remove the import step entirely. Coffee is an autonomous CRM agent that ingests contact and activity data in real time from Google Workspace and Microsoft 365, without a CSV, a mapping template, or a scheduled scenario.<\/p>\n<p>After you connect a Google or Microsoft account, the Coffee Agent scans emails and calendar events to auto-create contacts and companies, enrich records with job titles, funding data, and LinkedIn profiles, and log last and next activity autonomously. The result is a contact database that stays current without human intervention, saving the manual data work described earlier.<\/p>\n<p>For teams committed to monday.com, Coffee operates as a Companion App through API and Zapier, writing enriched contact data and activity logs back to the monday.com board as a tireless background worker. For teams open to a new system of record, Coffee\u2019s Standalone CRM replaces monday.com\u2019s contact boards with an agent-managed pipeline. Both deployment models fall under Coffee\u2019s SOC 2 Type 2 and GDPR compliance framework, and data is never used to train public models.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Replace your import workflow with Coffee\u2019s real-time agent<\/a> and keep monday.com updated automatically.<\/p>\n<h2>Coffee vs Continued Automation<\/h2>\n<p>The following comparison focuses on ongoing labor, setup effort, and long-term scalability, including Coffee\u2019s agent-based model, so you can see which path reduces operational burden as your contact volume grows.<\/p>\n<table>\n<thead>\n<tr>\n<th>Approach<\/th>\n<th>Ongoing Labor<\/th>\n<th>One-Time Setup<\/th>\n<th>Long-Term Scalability<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Manual CSV upload<\/td>\n<td>High, with weekly effort per rep<\/td>\n<td>Low<\/td>\n<td>Fails above roughly 500 contacts per month<\/td>\n<\/tr>\n<tr>\n<td>Make or Zapier automation<\/td>\n<td>Medium, with monitoring and error resolution<\/td>\n<td>Medium, about 2\u20138 hours<\/td>\n<td>Constrained by task or operation caps and API rate limits<\/td>\n<\/tr>\n<tr>\n<td>Python API script<\/td>\n<td>Medium, with engineering maintenance<\/td>\n<td>High, 4\u20138 hours plus hosting<\/td>\n<td>Scalable but requires ongoing development resources<\/td>\n<\/tr>\n<tr>\n<td>Coffee Agent<\/td>\n<td>None, because the agent handles ingestion<\/td>\n<td>Low, connect a Google or Microsoft account<\/td>\n<td>Scales with team size through seat-based pricing and no task caps<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Validation Checklist After Implementation<\/h2>\n<ol>\n<li><strong>Duplicate rate:<\/strong> Run a deduplication report immediately after import or agent activation, and target a rate below 3%.<\/li>\n<li><strong>Field completeness:<\/strong> Verify that email, first name, last name, company, and owner are populated on more than 90% of records.<\/li>\n<li><strong>Pipeline accuracy:<\/strong> Cross-reference deal stages against rep-reported status in the first weekly review, because companies with complete CRM data achieve higher forecast accuracy.<\/li>\n<li><strong>Activity logging:<\/strong> Confirm that last-activity dates populate automatically instead of remaining blank.<\/li>\n<li><strong>Bounce rate:<\/strong> Send a validation email sequence to the imported list and keep hard bounce rate below 5%.<\/li>\n<\/ol>\n<h2>Scaling and Advanced Use Cases<\/h2>\n<p>As team size grows from 5 to 50 reps, the Python API approach needs load-balancing logic and a queue system such as Celery with Redis to stay within monday.com\u2019s rate limits. Make and Zapier scenarios require plan upgrades as operation and task volumes increase, so both paths add engineering or tooling cost linearly with scale.<\/p>\n<p>Advanced workflows such as routing inbound leads from a website visitor identification pixel directly into monday.com contact boards, or triggering enrichment on deal-stage changes, require either custom API development or a platform that handles these patterns natively. Coffee\u2019s Visitor Identification feature identifies anonymous website traffic as named prospects and routes them into the CRM with enrichment pre-filled, closing the loop from website visit to outbound action without a separate import step. The List Builder feature supports natural-language prospecting queries such as \u201cFind VPs of Sales in North America at companies with $10M+ funding\u201d that populate contact boards directly, replacing manual list-building and CSV uploads for outbound campaigns.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678186019-5cc1a76ac78e.gif\" alt=\"Build people lists automatically with Coffee AI CRM Agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Build people lists automatically with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678641499-bad085f8165f.gif\" alt=\"Building a company list with Coffee AI\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Building a company list with Coffee AI<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Learn how Coffee scales with your team<\/a> and keeps contact management efficient as volume grows.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the fastest way to import contacts into monday.com in bulk?<\/h3>\n<p>The fastest one-time method uses monday.com\u2019s native data importer, which accepts CSV and Excel files and provides a column-mapping interface during upload. To prevent duplicates, enable the \u201cUpdate existing items\u201d option and designate the email address column as the unique identifier before confirming the import. For recurring imports, a Make or Zapier scenario that watches a Google Sheet and pushes new rows to monday.com automatically runs faster than repeated manual uploads and removes the need to prepare a new file each cycle.<\/p>\n<h3>How do I prevent duplicate contacts when importing into monday.com?<\/h3>\n<p>Duplicate prevention requires a check before record creation, not after. In Zapier, add a \u201cSearch Items by Column Value\u201d step that uses the email address as the lookup key, then add a Filter step that halts the workflow if a matching record already exists. In Make, use the monday.com \u201cSearch Items\u201d module upstream of the \u201cCreate Item\u201d module and route only non-matching records. For the native importer, the \u201cUpdate existing items\u201d toggle performs a similar function but only matches on the column you designate as the unique key. Post-import, schedule a weekly deduplication scan to catch any records that bypassed the filter because of formatting inconsistencies.<\/p>\n<h3>Can Coffee work alongside monday.com instead of replacing it?<\/h3>\n<p>Yes. Coffee operates in two deployment modes. As a Companion App, the Coffee Agent connects to monday.com through API and Zapier, writing enriched contact records, activity logs, and meeting summaries back to the monday.com board automatically. In this mode, monday.com remains the system of record while Coffee handles the data-in process that would otherwise require manual imports or scripted automation. As a Standalone CRM, Coffee replaces monday.com\u2019s contact and pipeline boards entirely with an agent-managed system. Both models use seat-based pricing with no task or operation caps.<\/p>\n<h3>What data quality thresholds should RevOps teams enforce before a bulk import?<\/h3>\n<p>The quality gate described in Stage 1, with duplicate rate below 3%, field completeness above 90%, and bounce rate below 5%, should be enforced before every import. In addition, enforce 100% standardized field formats for dates, phone numbers, and country names, and ensure every record has a named owner. Files that fail any of these thresholds should return to the data-prep stage rather than move forward. Post-import, monthly audits that track email validity, duplicate rate, and activity-date population confirm whether the import maintained or degraded board quality over time.<\/p>\n<h3>When should a company stop using CSV imports and switch to an automated agent?<\/h3>\n<p>Three signals indicate that the transition point has arrived. Weekly import preparation time exceeds four hours across the team, the board\u2019s duplicate rate climbs above 15% between cleanup cycles, or activity data such as emails, calls, and meetings is consistently missing from contact records because no import process captures unstructured interaction history. At that point, the operational cost of maintaining the import workflow exceeds the cost of deploying an agent that ingests data continuously from email and calendar sources. For most small-to-mid-size teams, this threshold appears between 300 and 500 active contacts managed by two or more reps.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stop manual CSV uploads for good. Coffee shows you how to automate monday.com CRM bulk contact imports and cut duplicate rates below 3%.<\/p>\n","protected":false},"author":11,"featured_media":5498,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5499","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/5499","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/comments?post=5499"}],"version-history":[{"count":0,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/5499\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/5498"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=5499"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=5499"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=5499"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}