Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Social intelligence for 8 networks: read posts, creators and trends, then create from them.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Funciona en Claude para web, escritorio y móvil:
https://mcp.orchyn.com/mcpAbre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"orchyn": {
"command": "npx",
"args": [
"-y",
"@orchyn/mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add --transport http orchyn https://mcp.orchyn.com/mcpclaude mcp add orchyn -- npx -y @orchyn/mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"orchyn": {
"url": "https://mcp.orchyn.com/mcp"
}
}
}Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"orchyn": {
"command": "npx",
"args": [
"-y",
"@orchyn/mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"orchyn": {
"type": "http",
"url": "https://mcp.orchyn.com/mcp"
}
}
}Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"orchyn": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@orchyn/mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
ChatGPT solo se conecta a servidores en internet:
https://mcp.orchyn.com/mcpLos nombres de los menús pueden variar un poco según el plan y con el tiempo.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Todavía no hemos leído la lista de herramientas de este servidor; revisamos los endpoints por turnos.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con el dominio orchyn.com (el registro comprueba quién es dueño del dominio)
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; leemos la lista de herramientas cuando revisamos el endpoint
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Funciona de las dos formas: instálalo en tu computadora o conéctate a la versión en la nube.
El repo de GitHub cambió por última vez –, y la última versión (1.26.17) llegó al registro hace 2 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, sync. labs
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Lipsync videos, dub them into other languages, make talking videos and open sync. Studio.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
MCP (Model Context Protocol) server for nooticr.
Gives an AI assistant three things: it can read real social posts across ten networks (TikTok, Instagram, YouTube, X, Reddit, LinkedIn, Douyin, Xiaohongshu, Weibo, Bilibili), understand them — transcript, video frames, comments, the numbers — and make something from what it learned: hooks, variants to film, a scored draft, a repurposed thread.
The understanding is your model's, not ours. Every tool here fetches material and hands it over with an account of what to do with it; none of them ask a model of ours for an opinion first. You pay for the fetch and nothing else.
It also monitors a name: search_mentions sweeps nine of those networks for every comment that says your brand, inside a date window you choose, and search_spoken_mentions reads the words actually said out loud in TikTok, YouTube and Douyin videos for the mentions that were never typed anywhere.
Runs over stdio locally or as a hosted connector at https://mcp.nooticr.com/mcp. Billed against your nooticr credits; new accounts get 20 free.
Claude Code — register the marketplace, then install the plugin:
/plugin marketplace add Nooticr/nooticr-mcp
/plugin install nooticr@nooticr
Claude Code / CLI without the plugin:
claude mcp add nooticr --user -- npx -y @nooticr/mcp
npx @nooticr/mcp login # one-time sign-in (Google)
Cursor / any stdio MCP client (claude_desktop_config.json, .mcp.json, …):
{
"mcpServers": { "nooticr": { "command": "npx", "args": ["-y", "@nooticr/mcp"], "env": { "NOOTICR_BASE_URL": "https://api.nooticr.com" } } } }
170 tools, grouped by what you are trying to do. Prices are in nooticr credits and match what the server actually charges.
The job tools under Answer a question you actually have are not endpoint wrappers. Each names a job, fans out over the calls that job needs, groups the evidence by whatever you are deciding about, gives every item an id a follow-up tool can act on, and hands the reading to your model rather than to ours. They fan out, so they cost the sum of what they fetched and every one of them caps that fan-out with an argument.
| Tool | Credits | What it is for |
|---|---|---|
get_social_post | 1 | The post's facts and media — contentType, title, caption, author, stats, direct media URLs, plus an inline thumbnail. Use when you want the post itself and nothing interpreted. |
get_post_transcript | 1 | The words actually spoken. Where the platform publishes a caption track (TikTok, Douyin, YouTube) it is read as-is — exact rather than inferred, without processing the video; everywhere else, and where a caption track is missing, the post's own audio is transcribed on the same call (posts up to 30 minutes), which needs speech-to-text configured on the server. No transcript, no charge. format: "srt" or "vtt" also returns a ready caption file built from the track's own timing, at no extra cost. Use before any analysis when the wording matters. |
get_post_frames | 2 | Frames sampled evenly across a post's video, returned as images you can actually look at — not a description of them. ffmpeg opens the stream directly rather than downloading it, so HLS works and an expired link is re-resolved on the spot. Verified live at 3/3 on TikTok, YouTube, Instagram, Douyin and X; Reddit works on video posts. A carousel or slideshow returns its own images unchanged. Each frame costs roughly 1,200 tokens of your context. |
get_post_comments | 2 | Top comments plus the themes the platform clusters them into, with which ones the creator pinned or liked. Use when you want to read what people wrote. |
| Tool | Credits | What it is for |
|---|---|---|
analyze_post_text | 2 | The post's transcript, caption and stats — everything but the pictures. Two fetches: the post itself (1, as get_social_post) and get_post_transcript (1). Use when the words and numbers answer the question. |
analyze_post | 3 | Frames sampled across the video, as images your model can actually look at, plus the transcript. Two fetches: get_post_frames (2) and get_post_transcript (1). Use when the visuals are the point — framing, editing, on-screen text. |
understand_social_post | 3 | The same two fetches, asked for a description of what physically happens on screen rather than why it works. Use when you need the events, not the strategy. |
summarize_post_comments | 2 | The comment section, every comment with a stable id, and the taxonomy to label them with — sentiment, and whether each is praise, a complaint, a bug report, a question, a request, a comparison or spam. The same get_post_comments call, at the same price as reading them directly. Pass verbatim: true to have every item rendered whole for quoting rather than clipped to fit; same price. |
show_comment_review | free | Draws the classifications your model produced — every comment with its sentiment and category, filterable and selectable. Makes no requests; it only renders what you pass it. |
show_post_analysis | free | Draws the analysis your model wrote from analyze_post/analyze_post_text/understand_social_post — hook strength, script structure, quotable lines, suggested hashtags and more, whichever fields you produced. Makes no requests; it only renders what you pass it. |
compare_social_posts | 1 | The first of two to five posts, fetched with its stats, and the comparison left to you. Fetch the rest with get_social_post at 1 credit each. Use when performance differs and you need to know why. |
show_compared_posts | free | Draws the comparison your model wrote from compare_social_posts — each post scored, the winner marked, what differed and the next test worth running. Makes no requests; it only renders what you pass it. |
| Tool | Credits | What it is for |
|---|---|---|
discover_social_posts | 2 | Recent posts for a niche across nine networks, with inline thumbnails and limit/offset pagination. Use to find posts to look at. |
get_user_posts | 2 | One creator's recent posts with stats. Use to scan an account. |
search_creators | 2 | People by what they make — designers, developers, photographers, anyone with an audience — found by craft, niche or keyword. Use when you know the kind of person but not their names. Searches TikTok, Instagram and Xiaohongshu; not YouTube, Douyin, X, Reddit or LinkedIn, which cannot be creator-searched upstream. |
get_similar_creators | 2 | Lookalikes for a creator that already works. |
suggest_creator_identity | 2 per network searched, plus 2 to find the handle (6 by default) | The same person on the other networks, from one handle you already know — with the evidence for each: a link published in both bios, one bio naming the other account, an identical handle, a matching display name. Suggests, never merges: nothing is stored, a human confirms, and two accounts' follower counts are never added together. TikTok, Instagram and Xiaohongshu. |
discover_sounds | 2 | Trending audio with playable previews. Sound is a major ranking signal on TikTok. |
discover_hashtags | 2 | What to tag with. TikTok comes from the Creative Center trend board — volumes plus rising/cooling/steady. The other eight searchable networks have no trend board upstream, so tags are counted across a live sweep of a niche you name: real and current, but a single sample, so no rising/cooling signal. source says which you got. LinkedIn cannot be swept. |
find_hook_pattern | 2 | A creator's recent posts, so their opening lines can be read as a set and turned into fill-in-the-blank templates. One get_user_posts call. |
search_mentions | 2 per network (5 for Xiaohongshu) | Brand monitoring. Every *comment* that names a term, across nine networks at once, grouped under the post it was left on. A brand is named far more often in the replies than in a caption, so the comment is the unit — not the post. Takes a since date to read a past window, and pages with offset/pageSize so a nine-network sweep does not arrive all at once. Does not read speech inside a video — search_spoken_mentions does, on TikTok, YouTube and Douyin. |
watch_creator | free | Add a creator to your watchlist. Stores the handle only — nothing is fetched. |
unwatch_creator | free | Drop a creator from the watchlist. |
list_watchlist | free | Who you are watching: handle, platform, note, and when you last caught up on each — and which saved creator identity each account belongs to, grouped as one person in byIdentity. |
save_creator_identity | free | Once the user has confirmed that accounts on different networks are one person (for instance from suggest_creator_identity's evidence), save them as one creator identity: name, accounts (each {platform, handle, evidence?}), note. Stores exactly what it is given; an account already in another identity is refused with that identity named, never moved. |
list_creator_identities | free | Every creator identity your workspace has saved, with its accounts and the evidence recorded for each. Where an identityId comes from. |
update_creator_identity | free | Rename a saved identity, change or clear its note, or add and remove accounts. An identity keeps at least one account. |
delete_creator_identity | free | Delete a saved identity. Only the grouping goes: the accounts and their watchlist entries stay. |
watch_creator_identity | free | Watch a person rather than one account: every account of a saved identity goes on your watchlist as its own entry with its own baselines, grouped under the identity. Nothing is fetched. |
unwatch_creator_identity | free | Take every account of a saved identity off your watchlist. Accounts watched on their own stay. |
catch_up_watchlist | 2 per creator | What everyone you watch has posted since your last catch-up. Compares against the snapshot taken last time and moves it forward, so it answers "what is new" rather than "what exists". |
create_brand_watch | free to call | Schedule a recurring sweep and get emailed only what is new. kind: "mentions" (default) is a recurring search_mentions sweep for term/platforms; kind: "competitor" is a recurring get_user_posts check on one creator (handle/platform), mailing only what beats their own recent median; kind: "report" runs job: {tool, args} — niche_report (mailed as written every run) or discover_social_posts (only unseen posts) — on the schedule; kind: "portfolio" sweeps up to 25 terms (your brand, products, competitors) across the same networks under one budget and mails one digest with each term's share of voice. Two calls by design: the first returns the quote (cost per run, cadence, cost per day) and a confirmationToken; nothing is created or charged until you call it again with confirm: true and that token. Each run then bills what the server charges for that work — 2 credits per network (5 for Xiaohongshu) for mentions, a flat 2 credits for competitor, for a report 3 for niche_report (a run writes the report it mails, where the niche_report tool here hands the posts to your model for 2) or 2 for discover_social_posts (plus 1 per post opened for comments), and a mentions sweep per term for a portfolio — whose budget is split evenly and trims networks, never terms, and which refunds any term whose sweep fails. appIds ties a watch to products of your workspace (send it on both calls); without it the watch is workspace-wide. |
| list_brand_watches | free | Every brand-monitoring watch in your workspace (your teammates' included): term, networks, cadence, cost per run, credits sp
Solo se muestra el principio; lee el resto en el repo