keyapi-tiktok-influencer-discovery

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

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Prerequisites

Requirement	Details
KEYAPITOKEN	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: INLINECODE0
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

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MCP Server Configuration

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

CODEBLOCK0

Setup (one-time):

CODEBLOCK1

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

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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	INLINECODE3	Initial discovery, broad prospecting
Verify an influencer's identity and resolve IDs

get_influencer_detail | ID resolution (userid + uniqueid), 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

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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.
• INLINECODE32 — 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:

CODEBLOCK2

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):

CODEBLOCK3

Example — search influencers:

CODEBLOCK4

Example — filter influencers with analytics (all pages):

CODEBLOCK5

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

CODEBLOCK6

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.

CODEBLOCK7

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:

CODEBLOCK8

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. 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

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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
INLINECODE72	Success	Continue workflow normally
INLINECODE73

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 |