ig-extract-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ig_extractA | Extract konten Instagram apapun: post, reel, carousel banyak slide. Return caption + media URLs per slide + metadata (owner, likes, date). Support /p/ dan /reel/. Pakai instaloader → gallery-dl fallback. Butuh login untuk private/rate-limited. |
| screener_parseA | Parse deskripsi screener bebas (caption IG, OCR dari gambar/carousel, atau teks manual) jadi filter terstruktur. Handle pola umum: PER<15, PBV<1, ROE>15%, volume>2x, RSI<30, foreign net buy, sektor, harga, ARA, dll. Output siap pakai untuk screener builder. |
| screener_from_imageB | OCR gambar screener (screenshot IG carousel — banyak slide sekaligus, tabel filter, atau cover reel) jadi filter terstruktur. Kirim image URL atau base64. AI baca angka + kriteria di tiap slide, return filter JSON gabungan. |
| ig_to_screenerA | One-shot workflow: URL IG apapun (post/reel/carousel banyak slide) → auto extract caption + carousel → OCR tiap slide → parse jadi filter screener terstruktur. Paling gampang buat user — cukup kasih link IG. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
ig_extract and ig_to_screener are fairly distinct, but screener_parse and screener_from_image overlap because both appear to handle image/carousel OCR input. Descriptions help clarify the intended input type, but an agent could still hesitate when given an image.
All names are lowercase snake_case, which is readable, but the naming patterns are mixed: noun_verb (ig_extract, screener_parse) vs. prepositional phrases (screener_from_image, ig_to_screener). There is no consistent verb-first or verb_noun convention across the set.
Four tools is well-scoped for a focused pipeline: raw IG extraction, text parsing, OCR parsing, and a combined one-shot workflow. Each tool earns its place without bloat or an overly thin surface.
The set covers the full intended workflow from an Instagram URL or raw text/image input to structured screener filters. Post, reel, carousel, OCR, and manual text paths are all addressed, with ig_to_screener covering the end-to-end case.