Rampify MCP Server
Rampify MCP Server lets an AI coding agent audit, research, plan, implement, and verify SEO/content work for a website using live crawl, Search Console, keyword, and spec data.
Analyze a specific page's SEO: GSC performance, detected issues, opportunities, and recommendations.
Get site-wide SEO issues and health score, with severity/type filters.
Trigger fresh site crawls to refresh SEO data after deployments.
Generate schema.org JSON-LD and optimized meta/OG tags for pages.
Retrieve Google Search Console insights: top pages, query opportunities, and content recommendations.
Research keywords: search volume/CPC/trends, related suggestions, and create/manage keyword clusters.
Create, retrieve, update, and list feature specs with tasks, acceptance criteria, status, and next actions.
Create content specs linked to keyword clusters.
Generate actionable content optimization instructions from keyword audits.
Link git commits to specs/tasks and generate conventional commit messages.
Get homepage security analysis and remediation steps (private beta).
Integrates Google Search Console data into the editor, providing performance metrics, clicks, impressions, rankings, and content strategy insights based on real search data.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Rampify MCP Server audit my homepage for SEO issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Rampify MCP Server
Turn your coding agent into a marketing agent. Rampify connects Claude Code, Cursor, GitHub Copilot, or any MCP client to your website's search data. It scans your site, writes what to fix or build as specs your agent ships, and confirms each fix on the next scan, across Google search and AI answers.
Website · Product · Docs · Connect
How it works
Scan. Rampify crawls your site and reads your Google Search Console performance and keyword data.
Spec. Each gap becomes a spec: the pages and files it affects, the tasks, and how to tell it's done.
Ship. Your agent makes the change in your repo and links the commit to the spec.
Verify. The next scan checks the fix and closes the spec, or reopens it if the problem comes back.
Related MCP server: Google Search Console MCP Server
Why Rampify
Real data, built in. Keyword volumes (via DataForSEO), your Search Console performance, and a live crawl of your own site. No separate data servers to bolt on.
Specs, not suggestions. Findings arrive as work your agent can pick up, with the same paper trail as your engineering: tasks, commits, and a status you can trust.
Fixes are checked, not assumed. A spec is done when the next scan confirms it, not when someone says so.
Find your blind spots. Playbooks like the Category Visibility Check ask AI assistants the questions your buyers ask, show whether they name you, and file a spec for the biggest gap.
Connect
Rampify is a remote server at https://www.rampify.dev/api/mcp. Nothing to install or update, and you sign in with OAuth, so there are no API keys to manage.
Claude Code
claude mcp add --transport http rampify https://www.rampify.dev/api/mcpCursor (.cursor/mcp.json), or any client that takes a URL
{
"mcpServers": {
"rampify": { "url": "https://www.rampify.dev/api/mcp" }
}
}VS Code and GitHub Copilot: install the Rampify extension. It connects Copilot in one click and shows your specs in the sidebar.
claude.ai: add a custom connector with the URL above.
Step-by-step for each client: Connecting guide →
Things to ask once you're connected
"Crawl my site and show me which pages are invisible when buyers ask AI assistants about my category."
"Research keywords for /pricing, then write the content spec."
"Audit the meta and schema on my top pages, and open a PR with the fixes."
"What should I work on next?"
What your agent can do
More than 50 tools: crawl and scan pages (meta, schema, content, Core Web Vitals), page-level intelligence, Search Console performance, keyword research and clusters, title, meta and JSON-LD generation, feature specs with tasks and linked commits, research playbooks, and campaign tracking. See the full tool catalog →.
Learn more
Product: https://www.rampify.dev/seo-mcp
Tool catalog: https://www.rampify.dev/docs/mcp
Connecting guide: https://www.rampify.dev/docs/mcp/connecting
Pricing (free to start): https://www.rampify.dev/pricing
About this repo
This repo holds Rampify's public listing metadata: server.json for the official MCP Registry (dev.rampify/rampify-mcp), the Cursor plugin manifest (.cursor-plugin/, mcp.json), and glama.json. The server itself runs at rampify.dev.
Looking for the old @rampify/mcp-server npm package? It is deprecated in favor of the hosted remote server above (zero install, always current). Its source is in this repo's history, last at 9e160f0.
Available Tools
19 toolscrawl_siteAInspect
Trigger a fresh site crawl and analysis. Use this after deploying changes to refresh SEO data. Crawls the entire site, checks all URLs, detects issues, and updates the database with current SEO status.
Inputs: domain.
create_content_specAInspect
Create a page-type feature spec linked to a keyword cluster. The spec carries the content strategy (outline, goals, voice, inspiration); keyword data is resolved dynamically from the cluster at read time.
Use this after creating keyword clusters to generate actionable content briefs. Each cluster maps to one page — the spec tells an AI agent exactly what to build. The spec auto-creates tasks: write content, optimize for keywords, add schema/meta.
The response includes the spec_id. Use get_feature_spec with that ID to retrieve the full spec with live keyword data, volumes, and GSC performance.
Inputs: cluster_id (required), domain, project_id, title, +6 more.
create_feature_specAInspect
Create and save a feature specification to Rampify.
IMPORTANT: Before calling this tool, YOU (Claude) must generate the complete structured spec from the user's description and your codebase context. Do not pass raw natural language — populate all fields:
Infer affected_files from open files and the codebase structure
Infer tech_stack from package.json and imports
Generate 3-5 acceptance criteria covering happy path, edge cases, and error handling
Break implementation into 3-8 concrete tasks with file references
Write ai_context_summary to help future AI agents understand the approach
Set next_action to the single most important first step
Inputs: title (required), domain, project_id, description, +9 more.
create_keyword_clusterAInspect
Create a strategic keyword cluster with its keywords in a single call. Each cluster groups related keywords sharing search intent and serves as a content brief with: strategic rationale, competitive landscape, target content type, and keyword assignments.
Always include the keywords array — keywords that don't exist yet are auto-created as target keywords. Returns keywords_assigned and keywords_created counts to confirm what was added.
Use this during keyword research conversations to organize findings into actionable clusters. Each cluster maps to one page and guides content creation.
Inputs: name (required), domain, project_id, description, +7 more.
generate_metaCInspect
Generate optimized meta tags (title, description, OG tags) for a page. Analyzes page content and provides recommendations for SEO-optimized meta tags based on actual content, headings, and topics.
Inputs: url_path (required), domain, include_og_tags, framework.
generate_schemaAInspect
Auto-generate structured data (schema.org JSON-LD) for any page. Detects page type (Article, Product, FAQ, etc.) and generates appropriate schema with validation. Returns ready-to-use code snippets.
Inputs: url_path (required), domain, schema_type.
get_commit_messageAInspect
Generate a conventional-commits-style message from spec/task context. No external AI call — derived from structured spec data already in the database.
Returns a ready-to-use commit message string with type(scope): subject, spec/task references, file list, and co-authorship attribution. Use this before committing to get a well-formatted message.
Inputs: spec_id (required), domain, project_id, task_id, +1 more.
get_feature_specAInspect
Retrieve a feature specification from Rampify. Use this when starting work on a feature to understand what to build, which files to touch, and what the acceptance criteria are.
Two lookup modes:
spec_id: fetch a specific spec with full criteria and tasks (use this when you know the ID)
search: keyword search across titles and descriptions (returns a list; follow up with spec_id for full details)
Inputs: domain, project_id, spec_id, search, +2 more.
get_gsc_insightsCInspect
Get Google Search Console performance insights with AI-powered content recommendations. Returns top performing pages, query opportunities (improve CTR, rankings, keyword gaps), and actionable recommendations for what content to write next.
Inputs: domain, period, include_recommendations.
get_issuesBInspect
Get SEO issues for entire site with health score. Returns health score (0-100), categorized issues by severity, and prioritized recommendations for fixes.
Inputs: domain (required), filters.
get_keyword_clustersAInspect
Retrieve keyword clusters with keywords, volume data, GSC performance, and audit status. Supports filtering by target_url (exact match), name (partial match), or cluster_id.
Use target_url to find the cluster for a specific page (e.g., target_url: "/" for the homepage cluster). Without filters, returns all clusters.
Inputs: domain, project_id, target_url, name, +1 more.
get_page_seoBInspect
Get comprehensive SEO data and insights for a specific page. Returns performance metrics from Google Search Console, detected issues, optimization opportunities, and actionable recommendations.
Inputs: domain (required), url_path, file_path, content.
get_security_contextBInspect
Get security analysis for a site (homepage scan only). Returns security score, detected issues (headers, SSL, exposed files, third-party scripts), and actionable remediation steps. PRIVATE BETA.
Inputs: domain (required).
link_commitAInspect
Link a git commit SHA to a feature spec and optionally a task. Creates full traceability: code -> commit -> task -> spec.
Deterministic workflow for AI agents:
Retrieve spec via get_feature_spec (spec_id is now in context)
Implement changes and commit code
Run
git rev-parse HEADto capture the exact SHARun
git remote get-url originand normalize SSH URLs to HTTPSCall this tool with the SHA, repo_url, and spec_id (and task_id if applicable)
The commit is recorded in the spec's related_commits array and commit_count is incremented. If task_id is provided, the commit is also linked to the task with a last_commit_at timestamp.
Inputs: spec_id (required), commit_sha (required), domain, project_id, +2 more.
list_feature_specsAInspect
Browse and filter all feature specifications for a project. Returns an overview of specs with status, priority, and task progress.
Use this to answer questions like "what's in progress?", "what's planned?", or "show me all high-priority specs". For full spec details (criteria, tasks, affected files), follow up with get_feature_spec using a spec_id from the results.
Inputs: domain, project_id, status, priority, +5 more.
lookup_keywordsAInspect
Look up search volume, competition, CPC, and monthly trends for a list of keywords using DataForSEO. Results are cached for 30 days — repeated lookups for the same keywords are free.
IMPORTANT: This calls a paid API. The response includes meta.from_cache and meta.from_api counts so you can see what hit the paid API vs cache. Always tell the user before calling this tool and how many keywords you're looking up.
Use this to validate keyword demand during research conversations, before creating clusters.
Inputs: keywords (required), domain, project_id, locale, +1 more.
optimize_contentAInspect
Generate specific optimization instructions for a page based on its keyword audit results. Runs the content audit, then for each failing check produces actionable fix instructions: add keyword to title, increase density by N occurrences, add internal/external links, vary keyword formatting.
Use this after reviewing a page with get_page_seo to get step-by-step instructions for improving keyword optimization. The AI agent reads these instructions and modifies the source files.
Constraint: instructions preserve content structure (headings, sections, flow). Only text, formatting, and links are modified.
Inputs: url_path (required), domain, project_id.
suggest_keywordsAInspect
Get related keyword suggestions for a seed keyword using DataForSEO. Returns up to 50 related keywords with search volume and competition data.
IMPORTANT: This calls a paid API. Always tell the user before calling this tool and what seed keyword you're using.
Use this to expand keyword research — find related terms, long-tail variations, and new cluster candidates.
Inputs: seed (required), domain, project_id, locale, +2 more.
update_feature_specAInspect
Update a feature spec to reflect actual progress. Mark tasks and criteria as complete, update spec status, and advance next_action.
Use this after completing work described in a spec task. Returns a suggested_commit message string.
Examples:
Mark a task complete: { spec_id, task_id, task_status: "completed" }
Update overall status: { spec_id, status: "in_progress" }
Mark a criterion verified: { spec_id, criterion_id, criterion_status: "verified" }
Inputs: spec_id (required), status, next_action, task_id, +4 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 19 tools
The tools cover two distinct domains (SEO and feature spec management), but create_content_spec and create_feature_spec have very similar names and overlapping descriptions, causing potential ambiguity. Most other tools are clearly distinct, but the presence of two 'create spec' tools without clear separation in naming or behavior detracts from clarity.
All tool names follow a consistent verb_noun pattern with underscores (e.g., crawl_site, create_keyword_cluster, get_page_seo). The naming is uniform and predictable, with no mixing of conventions. Minor variations in verb choice (e.g., 'generate' vs 'create') are acceptable and do not hinder understanding.
With 19 tools, the server covers a broad but well-scoped set of functionalities for SEO and feature specification management. The count is slightly on the higher side but still appropriate given the combined domains; each tool serves a distinct purpose, and there are no extremes.
The tool surface covers the key CRUD operations for feature specs (create, read, update, list, link commits) and a comprehensive set of SEO tools (crawl, analysis, keyword research, optimization). Missing delete operations and a dedicated content spec retrieval tool are minor gaps, but overall the server enables most workflows without dead ends.
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