Keyapi Tiktok Influencer Discovery

Discover, profile, and deeply analyze TikTok influencers — from keyword-based search to multi-dimensional performance intelligence covering follower trends, engagement rates, live-stream GMV, video performance, and competitive rankings.

Discover, profile, and deeply analyze TikTok influencers — from keyword-based search to multi-dimensional performance intelligence.

This skill powers end-to-end TikTok influencer research using the KeyAPI MCP service. It enables you to find creators by keyword or region, retrieve their profile and performance metrics, analyze historical growth trajectories, and benchmark them against ranking data — all through a single, orchestrated workflow.

Use this skill when you need to:

• Identify high-performing influencers for brand collaborations or affiliate campaigns
• Audit a creator's follower growth, engagement rate, and live-stream GMV history
• Build ranked shortlists and compare multiple creators across key performance dimensions
• Track historical trends for competitive intelligence and market positioning

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Prerequisites

Requirement	Details
KEYAPI_TOKEN	A valid API token from keyapi.ai. If you don't have one, register at the site to obtain your free token. Set it as an environment variable: export KEYAPI_TOKEN=your_token_here
Node.js	v18 or higher
Dependencies	Run npm install in the skill directory to install @modelcontextprotocol/sdk

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

MCP Server Configuration

All tool calls in this skill target the KeyAPI MCP server:

text

Server URL : https://mcp.keyapi.ai
Auth Header: Authorization: Bearer $KEYAPI_TOKEN

Setup (one-time):

bash

# 1. Install dependencies
npm install

# 2. Set your API token (get one free at https://keyapi.ai/)
export KEYAPI_TOKEN=your_token_here

# 3. List all available tools to verify the connection
node scripts/run.js --list-tools

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Analysis Scenarios

Select one or more nodes based on the research objective. Multiple nodes can be combined for cross-dimensional analysis.

User Need	Node(s)	Best For
Find influencers by keyword, category, or region	search_influencers	Initial discovery, broad prospecting
Verify an influencer's identity and resolve IDs	get_influencer_detail	ID resolution (user_id + unique_id), profile snapshot
Filter influencers with analytics (ER, GMV, followers, sales)	influencer_list_analytics	Data-driven shortlisting from large datasets
Full multi-dimensional performance audit	influencer_detail_analytics	Deep-dive due diligence on one or more creators
Analyze historical growth trends over time	influencer_trends_analytics	Growth velocity, follower trajectory, trend analysis
Review video content performance history	influencer_videos_analytics	Content strategy benchmarking, top-video analysis
Evaluate live-stream commerce history (GMV, viewers)	influencer_livestreams_analytics	Live commerce capability assessment
Examine promoted product portfolio and sales	influencer_products_analytics	Brand-fit assessment, niche/category alignment
Competitive ranking by followers, GMV, or ER	influencer_ranking_analytics	Leaderboard analysis, category benchmarks
Retrieve latest published videos with engagement stats	get_influencer_videos	Recent content monitoring, freshness check
Sample an influencer's follower list	get_influencer_followers	Audience quality sampling
Explore the accounts an influencer follows	get_influencer_following	Network and affinity analysis
Geographic breakdown of audience distribution	get_influencer_region	Geo-targeting fit for regional campaigns
Generate a shareable profile QR code	get_influencer_qr_code	Marketing material assets
Key milestone and achievement history	get_influencer_milestones	Growth storytelling, historical highlights

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Workflow

Step 1 — Identify Analysis Targets and Select Nodes

Clarify the user's objective and map it to one or more nodes from the table above. Typical entry points:

• Keyword discovery: Start with search_influencers, then optionally deepen with influencer_list_analytics for richer filtering.
• Direct profile lookup: Use get_influencer_detail with a known unique_id (@handle).
• Performance deep-dive: Combine influencer_detail_analytics + influencer_trends_analytics + influencer_videos_analytics.
• Live commerce evaluation: Use influencer_livestreams_analytics + influencer_products_analytics.
• Competitive ranking: Use influencer_ranking_analytics with appropriate category/region filters.

⚠️ Critical: Resolving user_id vs. unique_id

Two distinct identifier types are used across endpoints:

• unique_id — the user's public @handle (e.g., charlidamelio). User-visible, mutable.
• user_id — TikTok's permanent, immutable numeric UID assigned to each account.

When a workflow requires nodes that accept different identifier types, always call get_influencer_detail first using the unique_id to obtain both identifiers before proceeding.

Step 2 — Retrieve API Schema

Before calling any node, inspect its input schema to confirm required parameters, data types, and valid enumeration values:

bash

node scripts/run.js --schema <tool_name>

# Example
node scripts/run.js --schema influencer_list_analytics

For analytics nodes, pay particular attention to filter parameters (region, category, date range, follower range, etc.) and confirm the expected page_num/page_size fields.

Step 3 — Call APIs and Cache Results Locally

Execute the required tool calls and persist all responses to the local cache to enable result reuse across sessions and avoid redundant API calls.

Calling a tool (using scripts/run.js):

bash

# Single page call — result is cached automatically
node scripts/run.js --tool <tool_name> --params '<json_args>' --pretty

# Fetch all pages at once (auto-pagination)
node scripts/run.js --tool <tool_name> --params '<json_args>' --all-pages --page-size 50

# Force a fresh call, skip cache
node scripts/run.js --tool <tool_name> --params '<json_args>' --no-cache

Example — search influencers:

bash

node scripts/run.js --tool search_influencers \
  --params '{"keyword":"fitness","region":"US"}' --pretty

Example — filter influencers with analytics (all pages):

bash

node scripts/run.js --tool influencer_list_analytics \
  --params '{"region":"US","influencer_category_name":"Fitness"}' --all-pages

Example — get influencer's latest videos (cursor-based):

bash

# First page: offset=0
node scripts/run.js --tool get_influencer_videos \
  --params '{"unique_id":"charlidamelio","offset":"0"}' --pretty
# Next page: use max_cursor value from previous response as offset

Pagination for analytics endpoints:

All *_analytics endpoints use page_num (1-indexed) and page_size (max 10). run.js injects these automatically if not specified. Use --all-pages to let run.js iterate all pages and merge the results.

text

--page-num 1  --page-size 10   → first page (default)
--all-pages                    → all pages merged into one result

Note: get_influencer_videos, get_influencer_followers, get_influencer_following use cursor-based pagination via an offset parameter — not page_num/page_size. Pass "offset":"0" to start, then use the max_cursor (or min_time) value from the response as the next offset.

Cache directory structure:

text

.keyapi-cache/
└── influencers/
    └── {unique_id}/
        ├── detail.json                  # get_influencer_detail
        ├── analytics.json               # influencer_detail_analytics
        ├── trends.json                  # influencer_trends_analytics
        ├── videos_analytics.json        # influencer_videos_analytics
        ├── livestreams_analytics.json   # influencer_livestreams_analytics
        ├── products_analytics.json      # influencer_products_analytics
        ├── latest_videos.json           # get_influencer_videos
        ├── followers.json               # get_influencer_followers
        ├── following.json               # get_influencer_following
        ├── region.json                  # get_influencer_region
        ├── qr_code.json                 # get_influencer_qr_code
        └── milestones.json              # get_influencer_milestones
└── searches/
    └── influencers/
        └── {md5_of_query_params}.json   # search_influencers, influencer_list_analytics
└── rankings/
    └── influencers_{params_hash}.json   # influencer_ranking_analytics

Cache-first policy:

Before every API call, check whether a cached result already exists for the given entity and node. If a valid cache file exists, load from disk and skip the API call.

Cover image processing:

After each API call, scan all response image URLs. If any URL's host matches echosell-images.tos-ap-southeast-1.volces.com, collect those URLs and call batch_download_cover_images in a single batch request. Replace the original URLs in your working dataset with the converted URLs returned by this node.

Step 4 — Synthesize and Report Findings

After collecting all API responses (from cache or live calls), produce a structured research report:

1. Creator Profile Summary — Name, @handle, follower count, engagement rate, primary niche, and operating region.
2. Performance Analysis — Follower growth curve, average video views, engagement benchmarks, and live-stream GMV history.
3. Content Strategy Insights — Top-performing video themes, posting cadence, product promotion patterns, and audience interaction quality.
4. Competitive Positioning — Ranking within category/region, peer comparisons when analyzing multiple creators.
5. Actionable Recommendations — Best fit use cases (brand sponsorship, affiliate, live commerce), audience-campaign alignment, risk signals (follower authenticity, trend consistency).

Cross-reference multiple data sources where available — for example, correlate influencer_trends_analytics with influencer_livestreams_analytics to identify whether GMV peaks align with follower growth events.

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Common Rules

Rule	Detail
Pagination	All *_analytics endpoints use page_num (starts at 1) and page_size. Never use page 0.
Cover images	Batch-convert all image URLs from echosell-images.tos-ap-southeast-1.volces.com via batch_download_cover_images before storing or displaying.
Success check	code = 0 → success. Any other value → failure. Always check the response code before processing data.
Retry on 500	If code = 500, retry the identical request once after a brief pause before reporting the error.
Cache first	Always check the local .keyapi-cache/ directory before issuing a live API call.
ID resolution	When a workflow requires both user_id and unique_id, call get_influencer_detail first with the unique_id to resolve both.

author: KeyAPI license: MIT repository: https://github.com/EchoSell/keyapi-skills

Error Handling

Code	Meaning	Action
0	Success	Continue workflow normally
400	Bad request — invalid or missing parameters	Validate input against the tool schema; correct and retry
401	Unauthorized — token missing or expired	Confirm KEYAPI_TOKEN is set correctly; visit keyapi.ai to renew
403	Forbidden — plan quota exceeded or feature restricted	Review plan limits at keyapi.ai
404	Resource not found — influencer not indexed or ID incorrect	Verify unique_id / user_id; try search_influencers to locate the creator
429	Rate limit exceeded	Wait 60 seconds, then retry
500	Internal server error	Retry once after 2–3 seconds; if it persists, log the full request and response and skip this node
Other non-0	Unexpected error	Log the full response body and surface the error message to the user