# Dev.to Intelligence MCP > Dev.to Intelligence turns raw publishing data into a full content strategy guide. This MCP runs deep analyses to pinpoint optimal publishing times, discover overlooked high-opportunity tags, and map hidden audience overlaps across entire communities. It lets you stop guessing what works and start knowing. ## Overview - **Category:** marketing - **Price:** Free - **Tags:** devto, forem, content-strategy, publishing, engagement-analytics, audience-intelligence, syndication ## Description This connector gives your agent the ability to analyze Dev.to like a data science team would. Instead of just posting content, you can actually run strategic deep dives into performance metrics. You find out exactly when an article will perform best based on historical community activity, or which tags are generating buzz but haven't been written about enough. It also traces your readers across multiple topics, revealing adjacent communities that share your audience—stuff you couldn't find by just looking at the profile page. This level of proprietary insight is what makes Vinkius the go-to source for advanced content intelligence. ## Tools ### get_my_drafts Retrieves your unpublished Dev.to articles so you can review or resume unfinished drafts. ### analyze_engagement Calculates a deep engagement score for an article, weighting comments and unique commenters heavily to gauge quality. ### content_gap_analysis Finds content gaps by identifying tags that have high average interest but low existing article volume. ### get_followers Provides a list of your Dev.to followers for audience size and growth tracking. ### discover_top_authors Identifies the most influential authors within a specific tag, along with their best-performing content metrics. ### toggle_reaction Allows you to like, bookmark, or give special appreciation (unicorn) to an article or comment. ### update_article Modifies existing articles by changing titles, tags, or body content directly on the platform. ### audience_crossover Maps audience overlap between tags to suggest related communities for expanded reach. ### content_blueprint Extracts structural patterns from top articles, providing a 'recipe' for title format and tag combinations. ### get_article_by_slug Retrieves the full content of an article using its unique URL path instead of just the ID number. ### get_article Fetches the complete details and structure of a single Dev.to article by its numerical ID. ### get_comment_by_id Retrieves a single comment along with all its replies, useful for tracking specific discussion threads. ### get_comments Gathers every comment made on an article to analyze general community sentiment and feedback. ### get_followed_tags Lists the tags you are currently following, helping define your core content interest profile. ### get_latest_articles Retrieves a list of the most recently published articles across Dev.to for general trend monitoring. ### get_my_articles Lists all your own previously published articles on Dev.to, allowing you to audit your history and performance. ### get_podcast_episodes Browses multimedia content by listing podcast episodes published on the platform. ### get_reading_list Shows your saved or bookmarked articles, helping you manage your personal knowledge pipeline. ### get_tags Retrieves a list of popular tags on Dev.to to understand the current topics with high general readership. ### get_user Pulls the public profile information for any specified Dev.to user account. ### publish_article Creates and publishes a brand new article, allowing you to set titles, content, tags, and canonical URLs. ### publish_timing_analysis Runs a statistical analysis on a tag to determine the best day and time window for maximum visibility. ### search_articles Searches Dev.to content by specific tags, usernames, or popularity metrics to quickly evaluate topics. ## Prompt Examples **Prompt:** ``` Find the best time to publish an article about AI agents on Dev.to. ``` **Response:** ``` Running `publish_timing_analysis` for #ai-agents: analyzed 97 articles. Best day: Tuesday (42 avg reactions). Worst day: Saturday (8 avg reactions). Reading time sweet spot: 5-8 minutes (51 avg reactions in range). Publish on Tuesday morning for maximum visibility. ``` **Prompt:** ``` What topics does the #mcp community also care about? I want to expand my reach. ``` **Response:** ``` Running `audience_crossover` for #mcp: traced 15 active commenters. Top overlaps: #ai-agents (67%), #devtools (47%), #llm (40%), #automation (33%). Cross-post to these tags to reach the same audience with zero extra effort. ``` **Prompt:** ``` Publish my article about MCP protocol layers on Dev.to with proper canonical URL. ``` **Response:** ``` Using `publish_article`: article created successfully. Title: 'Beyond APIs: Autonomous Agents Need a Protocol Layer'. URL: https://dev.to/username/beyond-apis-autonomous-agents-need-a-protocol-layer-3edj. Tags: mcp, ai-agents, devtools, automation. Canonical URL set to your original blog post. ``` ## Capabilities ### Analyze Content Performance Deeply evaluates a single article to calculate an engagement score based on comment sentiment, unique commenters, and total reach. ### Discover Untapped Topics Calculates an opportunity score across multiple tags to identify high-interest topics that currently have low content competition. ### Map Audience Overlaps Traces commenter activity between different tags, revealing shared audience interests and cross-posting opportunities. ### Determine Best Publishing Times Runs statistical analysis across historical article data to recommend the optimal day of the week and reading time range for maximum visibility. ### Reverse-Engineer Structure Extracts structural patterns from top posts, detailing ideal title formats, best tag combinations, and optimal reading lengths. ## Use Cases ### The Quarterly Content Plan A content strategist needs to build out a quarter's worth of articles. They run `content_gap_analysis` across 15 tags, identifying three high-opportunity areas. Next, they use `discover_top_authors` on those topics to see who the established voices are, and then schedule their own posts using `publish_timing_analysis` for maximum impact. ### Reactivating Old Content A developer wrote a great article six months ago but it lost traction. They run `analyze_engagement` to see the original performance score and then use `content_blueprint` to update the title/tags, giving the piece a modern structure that matches current best practices. ### Targeting Niche Readers A writer wants their content about WebAssembly to reach people interested in AI. They run `audience_crossover` and find an overlap with #llm, guiding them to write a piece that speaks directly to both communities. ### Maximizing Visibility on Launch Day A team is ready to launch a major guide. They use `publish_timing_analysis` and find the sweet spot is Tuesday morning, two hours before the usual peak. They schedule the post using `publish_article` at that specific time. ## Benefits - Determine optimal posting times using `publish_timing_analysis`. Instead of manually checking analytics, this calculates the exact day/time window that historically maximizes visibility for a given tag. - Find untapped content opportunities with `content_gap_analysis`. This tool gives you an opportunity score, telling you exactly where to publish next—where interest is high but competition is low. - Map your audience using `audience_crossover`. You don't just know who reads about #ai-agents; you find out which other topics those same readers are interested in, letting you expand reach instantly. - Audit past performance by running `analyze_engagement` on any article. This gives a weighted score that weighs community comments much higher than simple reactions, telling you what truly resonated. - Structure your content using `content_blueprint`. It looks at the top-performing posts and hands you a 'recipe' for ideal titles, reading length, and tag combinations. ## How It Works The bottom line is you get data-backed answers to complex content questions without manually querying dozens of endpoints. 1. First, you tell your agent the scope of the analysis, whether it's a specific tag, a set of tags, or an article you want to benchmark. 2. The MCP runs complex, multi-step computations—like cross-referencing hundreds of timestamps and analyzing comment sentiment against unique commenter counts. 3. You get back actionable strategic reports: optimal publishing days, suggested title formats, or a list of hidden audience overlaps. ## Frequently Asked Questions **How do I use `publish_timing_analysis` to find the best posting day?** You provide the MCP with the specific tag you're targeting. It runs statistics on hundreds of historical posts and returns a detailed breakdown, telling you the optimal day of the week and reading time window. **Is `audience_crossover` better than just looking at my followers?** Yes. Your general follower list is static. The `audience_crossover` tool actively traces commenters across multiple tags, showing you *hidden* overlaps and potential communities you aren't even aware of. **What’s the difference between `get_article` and `search_articles`?** `get_article` pulls one article using its specific ID. `search_articles`, however, is designed to find multiple articles based on criteria like a tag or author name, giving you an overview of many options. **Can I use `content_gap_analysis` for any topic?** You must provide the MCP with several related tags. It then compares those tags to calculate which ones have high interest (engagement) but low existing article volume, identifying your best bet. **When running `get_my_articles`, what permissions are needed to access my drafts and published content?** The MCP requires explicit read/write API credentials linked to your Dev.to account. This ensures your agent has the authority to pull both your live articles and any unpublished drafts you want to review. **How does `analyze_engagement` process comments to give me a comprehensive sentiment score?** It doesn't just count words; it applies an engagement-weighted algorithm. This mechanism boosts the value of comments significantly over simple reactions, while also scoring unique commenters for community breadth. **What exactly does `content_blueprint` extract when analyzing top-performing articles?** It generates a structural recipe derived from real data. You get specific patterns on ideal title formats, optimal reading time ranges, and the best combinations of tags to use. **If I want competitive intelligence on who dominates a tag, how does `discover_top_authors` help?** This tool ranks authors by aggregating engagement data across multiple posts. It shows you which writers are most influential for a specific topic and what kind of content style performs best.