APPKITTIE MCP

AppKittie MCP

Connect app-store intelligence to coding agents with a Bearer API key, structured tool responses, and usage tracked in workspace credits.

API Keys

Endpoint

HTTP tools v1
https://api.getappniche.com/api/mcp/tools
Authorization: Bearer appkittie_...
Content-Type: application/json

Client config

Local
{
  "mcpServers": {
    "appkittie": {
      "url": "https://api.getappniche.com/api/mcp/tools",
      "headers": {
        "Authorization": "Bearer {APPKITTIE_API_KEY}"
      }
    }
  }
}

Tools

6 tools
search_apps1 creditDiscover apps by store, category, search text, and pagination.
get_app_detail1 creditInspect one app by canonical app_id such as apple:284882215.
get_keyword_difficulty10 creditsScore one keyword for ASO difficulty, popularity, and opportunity.
batch_keyword_difficulty10 credits / keywordCompare up to 10 keywords in one request.
get_app_reviews1 creditRead enriched review feed rows for monitored apps.
get_supported_countries0 creditsReturn supported country codes before planning research.

Agent Skill

Template
# AppKittie App Intelligence Skill

Use AppKittie MCP when the user asks for app-store discovery, competitor research, ASO keyword scoring, review signals, or market sizing.

Rules:
- Start with search_apps unless the user already gave a canonical app_id.
- Use get_app_detail before making claims about one app's downloads, revenue, rating, growth, ads, creators, or screenshots.
- Use get_keyword_difficulty for one keyword and batch_keyword_difficulty for 2-10 keywords.
- Use get_app_reviews only for review analysis on monitored apps.
- Cite app_id, store, category, rating_count, downloads_est_monthly, revenue_est_monthly, and growth_reviews_7d_pct when they support a recommendation.
- Keep credit usage visible in the answer when tools charge credits.
- Do not invent rank history, ad spend, creators, or reviews when AppKittie returns no source data.

Competitor brief

Find comparable apps and summarize their business signals.
When asked for a competitor brief:
1. Call search_apps with the target store/category/search terms.
2. Call get_app_detail for the strongest 3-5 app_ids.
3. Rank by downloads_est_monthly, revenue_est_monthly, rating_count, and growth_reviews_7d_pct.
4. Return a concise table plus risks, missing data, and one product opportunity.

ASO keyword pass

Score keywords and turn them into launch recommendations.
When asked for ASO keyword ideas:
1. Gather candidate keywords from the user's niche.
2. Use batch_keyword_difficulty for up to 10 terms per call.
3. Prefer high opportunity_score with moderate difficulty.
4. Return title/subtitle keyword picks and explain credit usage.

Review signal scan

Use monitored review feeds for product improvement themes.
When asked about review themes:
1. Use get_app_reviews with tracked_app_id when available, or store/store_id when the user gives app context.
2. Group negative reviews by topic and improvement area.
3. Quote only short snippets and label missing or demo data clearly.
4. Suggest the smallest product change that addresses the most frequent pain.

Request Snippets

3 examples
Search growing appssearch_apps
curl -X POST "https://api.getappniche.com/api/mcp/tools/search_apps" \
  -H "Authorization: Bearer {APPKITTIE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"store":"apple","category":"Social Networking","limit":5}'
Inspect one appget_app_detail
curl -X POST "https://api.getappniche.com/api/mcp/tools/get_app_detail" \
  -H "Authorization: Bearer {APPKITTIE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"app_id":"apple:284882215"}'
Compare keywordsbatch_keyword_difficulty
curl -X POST "https://api.getappniche.com/api/mcp/tools/batch_keyword_difficulty" \
  -H "Authorization: Bearer {APPKITTIE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"store":"apple","country":"US","language":"en","keywords":["habit tracker","daily planner","focus timer"]}'