Foglift MCP server

• Addresshttps://foglift.io/api/mcpnot in the official registry: answered when asked

No entry in the official MCP registry names this server. It answered on foglift.io when the Atlas asked at the address servers are usually mounted on, so it is the vendor's own. Clients and catalogues that list servers from the registry do not show it.

• Stateanswerschecked 2026-10-27
• Tools30
• Server calls itselffoglift
• Server version1.1.0
• Protocol version2025-03-26

On 2026-10-27 the server answered initialize and tools/list without a sign-in. First answer in 118 ms.

• Whether its tools do the product's job has not been judged yet.
• Checked on 1 of the 7 days it takes to judge reliability.

Said by rules from the measurements above. The likely first call is a model's pick from the tool list; read its description before relying on it.

• INITE MCP scoreafter a weeka score needs 7 daily checks: 1 so far
• Answeredmeasuring1 of the 7 daily checks it takes to print a share
• Median first answer146 ms
• Space in an agent's context≈ 5,602 tokensthe definitions of all its tools (30), which an agent carries on every turn
• Tool definitionsA · 100 of 100
• Typed results0 of 30 toolsdeclared outputSchema: results come typed, read once

• Every tool passes all six checks.

A program checks the server once a day: initialize and tools/list, no model. Tokens are the definitions' length at about four characters a token. The grade averages six checks of every tool's name, description, schema and annotations; it says how well the tools are described, not whether they work. The score weighs what was measured and nothing else: answering (25%), doing the product's job (25%), definitions (20%), context (10%), use (10%) and how readily a client connects (10%). A part that was not measured is left out, and a hidden instruction caps the score at 40. The state of vendors' MCP servers: method and numbers →

• scan_website

  Scan a website URL for SEO, GEO, and AI search readiness. Returns overall score, category scores (technical SEO, content quality, AI optimization, schema markup), and a list of specific issues with fix suggestions.

• batch_scan

  Scan multiple URLs in one request (max 10). Returns scores and issues for each URL. Requires API key.

• run_ai_visibility

  Run an AI visibility check — query AI models (ChatGPT, Claude, Perplexity, Gemini) with prompts related to your domain and see if they mention or recommend your brand. Returns per-model results with citation status and sentiment.

• get_ai_results

  Get historical AI visibility results for your domain. Shows how AI models have mentioned your brand over time, with sentiment and citation data.

• get_search_trace

  Get the ordered ChatGPT search trace for one AI visibility result, including search steps, queries, pages read, citation flags, run rollups, and the trailing 30-day prompt rollup when available.

• get_prompts

  Get your saved AI visibility monitoring prompts. These are the prompts used to check if AI models mention your brand.

• add_prompt

  Add a new prompt to monitor for AI visibility. The prompt will be periodically tested against AI models to check if they mention your brand.

• delete_prompt

  Delete a saved AI visibility monitoring prompt by its ID.

• get_models

  Get your enabled AI models and monitoring frequency settings. Account-level setting — applies to every brand on your account.

• set_models

  Update which AI models are enabled for monitoring and the monitoring frequency. Account-level setting — applies to every brand on your account.

• get_sentiment

  Get AI sentiment analysis results — shows how AI models talk about your brand (positive, neutral, negative) with trends over time.

• get_usage

  Get API usage statistics for your Foglift account — scan count, AI visibility checks, and quota remaining.

• get_scan_history

  Get historical scan results for a specific URL — shows how scores have changed over time.

• get_geo_monitor

  Get GEO monitoring data — track how your site's AI search optimization scores change over time.

• get_referrer_analytics

  Get AI-engine referrer analytics — total visits, per-engine breakdown, top landing paths, and daily trend for visitors arriving from ChatGPT, Claude, Perplexity, Gemini, Copilot, NotebookLM, and other AI engines.

• get_crawler_analytics

  Get observed AI crawler activity for a brand, including total visits, engine and crawler-category breakdowns, distinct pages reached, top paths, and the time trend. Use this for server-observed bot access; use get_referrer_analytics for…

• get_competitors

  Compare this brand with its named competitors using current and previous monitoring windows. Returns result mention rates and percentage-point changes, with incomplete rates omitted when an evidence window reaches its cap. Use this for…

• get_source_gap

  Find domains AI answers cite when this brand is absent, including which tracked competitors appear beside each source and an authority-based attainability signal. When authority is queued for refresh, authority and attainability remain…

• get_aeo_score

  Read the latest stored AI Readiness page score for a brand plus its score history and direction of change. This uses existing deterministic score evidence and does not run a new analysis or spend AI tokens.

• get_search_console

  Fetch Google Search Console or Bing Webmaster Tools data for the connected domain — top queries, top pages, and search demand with low AI visibility. Defaults to Google for backward compatibility.

• estimate_cost

  Estimate token cost before running run_ai_visibility. Accepts the same prompt/model shape as run_ai_visibility plus brand_id. Returns estimated_tokens, current_balance, and sufficient so agents can avoid overdrawing the account before a…

• get_recommendations

  Get AI visibility recommendation diagnostics for a Foglift brand without spending Foglift server-side recommendation tokens. Returns weak engines, weak prompts, citation/competitor gaps, already stored Foglift or customer-agent…

• submit_recommendation

  Submit a recommendation generated by your agent back to Foglift. Foglift stores it as source='customer_agent' for the brand and surfaces it in the dashboard and recommendation digest as generated by your agent. Include the fix, target…

• apply_recommendation

  Prepare the next action for a stored Foglift or customer-agent recommendation. For customer-site edits, returns a precise change-set instead of modifying the site. After applying, re-run run_ai_visibility and get_ai_results to verify…

• generate_content_brief

  Generate a content brief for a page in the target Foglift brand, using the brand context when brand_id is provided. Use this for recommendation follow-through on content_gap or visibility_boost fixes. Legacy workspace_id is accepted as an…

• report_issue

  Report a bug, unexpected behavior, question, or feature request to the Foglift team. You can describe this far more precisely than a human user - please do. Include exactly what you were trying to accomplish, the precise Foglift…

• whoami

  Return the authenticated Foglift account identity: user_id, plan, default workspace (name + domain), current token balance, and monthly allocation. Use this to confirm which account an API key is tied to.

• recommend_setup

  Calculate the sustainable monitoring prompt count from the authenticated Foglift account's persisted plan, active brands, scan frequency, and enabled engines. Returns the trusted prompt budget, category distribution, examples, and…

• apply_setup_recommendation

  Create the categorized monitoring prompts from a recommend_setup result for a specific Foglift brand. Requires brand_id, brand name, competitors, industry, and the recommendation output. Idempotent on retry. Legacy workspace_id is…

• recipe_run

  Run a named Foglift recipe against a specific brand. First available recipe: weekly-brand-health-report, which returns a weekly AI Readiness / AI Visibility report from existing scan and monitoring data.