Get comprehensive SEO data and insights for a specific page. Returns performance metrics from Google Search Console, detected issues, optimization opportunities, and actionable recommendations.

Get SEO issues for entire site with health score. Returns health score (0-100), categorized issues by severity, and prioritized recommendations for fixes.

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.

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.

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.

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.

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

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)

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" }

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.

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:

1. Retrieve spec via get_feature_spec (spec_id is now in context)

2. Implement changes and commit code

3. Run git rev-parse HEAD to capture the exact SHA

4. Run git remote get-url origin and normalize SSH URLs to HTTPS

5. Call 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.

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.

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.

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.

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.

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.

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.

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.

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.