Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Tells your agent which model to use and what it costs, across 46 providers and local runtimes.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, OPENROUTER_API_KEY, XAI_API_KEY, TYPESAFE_API_KEY, KOSHA_CACHE_TTL_HOURSValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"kosha-discovery": {
"command": "npx",
"args": [
"-y",
"@sriinnu/kosha-discovery"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value",
"GOOGLE_API_KEY": "your-value",
"OPENROUTER_API_KEY": "your-value",
"XAI_API_KEY": "your-value",
"TYPESAFE_API_KEY": "your-value",
"KOSHA_CACHE_TTL_HOURS": "your-value"
}
}
}
}Run this in your terminal
claude mcp add kosha-discovery --env ANTHROPIC_API_KEY=your-value --env OPENAI_API_KEY=your-value --env GOOGLE_API_KEY=your-value --env OPENROUTER_API_KEY=your-value --env XAI_API_KEY=your-value --env TYPESAFE_API_KEY=your-value --env KOSHA_CACHE_TTL_HOURS=your-value -- npx -y @sriinnu/kosha-discoveryFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"kosha-discovery": {
"command": "npx",
"args": [
"-y",
"@sriinnu/kosha-discovery"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value",
"GOOGLE_API_KEY": "your-value",
"OPENROUTER_API_KEY": "your-value",
"XAI_API_KEY": "your-value",
"TYPESAFE_API_KEY": "your-value",
"KOSHA_CACHE_TTL_HOURS": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"kosha-discovery": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@sriinnu/kosha-discovery"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value",
"GOOGLE_API_KEY": "your-value",
"OPENROUTER_API_KEY": "your-value",
"XAI_API_KEY": "your-value",
"TYPESAFE_API_KEY": "your-value",
"KOSHA_CACHE_TTL_HOURS": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
KOSHA_CACHE_TTL_HOURS envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account sriinnu, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, OPENROUTER_API_KEY, XAI_API_KEY, TYPESAFE_API_KEY
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (1.13.0) reached the registry 4 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, alalanet — check before you pay
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
Check packages, MCP servers and x402 endpoints before agents install or pay. Signed.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Tells your agent — or your code — which model to use and what it costs.
Website: kosha.srinivas.dev
kosha discovers models across 46 providers and local runtimes, finds your API keys wherever they already live (env vars, Claude CLI, Codex, gcloud ADC, AWS SSO), fills in pricing and context limits, and answers questions like *the cheapest model with tool use and 128k context that I actually hold a key for*. It ships as a TypeScript library, a CLI, an HTTP API, an OpenAI-compatible proxy with a spend ledger, and an MCP server.
It works with no API keys at all — discovery falls back to the public models.dev and LiteLLM catalogs, so kosha list is useful on a fresh machine.
npm install @sriinnu/kosha-discovery # library / server
npm install -g @sriinnu/kosha-discovery # global kosha CLI brew install sriinnu/tap/kosha # same CLI and MCP server, via Homebrew
Requires Node.js 22+.
import { createKosha } from "@sriinnu/kosha-discovery";
const kosha = await createKosha();
const models = kosha.models(); // ModelCard[] across every provider const cheapest = kosha.cheapestModels({ role: "image" }); // ranked by price, with missingCredentials const sonnet = kosha.model("sonnet"); // alias → canonical ID; undefined if unknown console.log(sonnet?.pricing); // { inputPerMillion: 2, outputPerMillion: 10, cacheReadPerMillion: 0.2, ... }
kosha discover # query every provider; writes ~/.kosha/cache and the manifest
kosha list --provider anthropic # read from the local cache kosha model sonnet # one model, alias-aware kosha routes claude-opus-5-5 # every serving route for a model (direct, OpenRouter, Bedrock, …) kosha cheapest --role embeddings # rank by price for a role kosha doctor --ci --model opus,gpt6 # fail CI if a model you pin is deprecated, retiring, or gone kosha spend --since 2026-09-01 # roll up the proxy's spend ledger kosha refresh # bypass the cache and re-discover kosha serve --port 3000 # HTTP API + proxy; binds 127.0.0.1 (see Proxy below)
Every command takes --json. kosha --help lists the rest.
After each discovery, a stable v1 manifest lands at ~/.kosha/registry.json:
jq '.models[] | select(.pricing.inputPerMillion < 0.1) | .modelId' ~/.kosha/registry.json
Fail a build when a model your code pins is deprecated, about to be retired, or listed by no provider any more:
- uses: sriinnu/kosha-discovery@v1
with: models: | claude-opus-5-5 gpt-6-sol sonnet
Pin it to a commit SHA rather than a moving tag, as you would any action. The action runs the kosha version it was released with, not latest, so a new npm publish does not change what your CI runs until you move the pin.
It runs kosha doctor --ci --model … against the public catalogs (no provider keys needed), writes a table to the job summary, and annotates each failing model. A reseller retiring one of its routes is reported but does not fail the build while the model's own provider still serves it; deprecation-window (default 30 days) sets how far ahead a sunset counts.
A weekly discovery run publishes a full snapshot — every provider, model, price and limit kosha can see without your keys — at a stable URL:
curl -sL https://github.com/sriinnu/kosha-discovery/releases/download/snapshot-latest/kosha-latest.json \
| jq '.modelCount, .providerCount'
It is a release asset rather than a file in the repository: at ~2.8 MB growing with every provider added, committing it weekly would put roughly 150 MB of already-stale data a year into a repo people are meant to clone. Dated snapshot-YYYY-MM-DD pre-releases keep a short trail for diffing, pruned to the two most recent, and an older one is only removed once a newer one exists.
Each snapshot's release notes list what changed since the previous one — new models, repricings, new deprecations, removals. The same diff runs locally with pnpm snapshot:diff old.json new.json.
GET /api/models?provider=&originProvider=&mode=&capability=
GET /api/models/:idOrAlias GET /api/models/:idOrAlias/routes GET /api/models/cheapest?role=… GET /api/capabilities GET /api/providers[/:id] GET /api/roles GET /api/resolve/:alias GET /api/discovery-errors GET /api/discovery[/delta|/watch|/cheapest|/binding] (stable v1 contract) POST /api/refresh GET /health GET /metrics GET /proxy/v1/models POST /proxy/v1/chat/completions
Parameters and response shapes: docs/api.md.
kosha serve also exposes an OpenAI-compatible endpoint at /proxy/v1. Point any OpenAI SDK at it; the proxy resolves the model or alias, picks a provider you hold credentials for, injects the upstream key, forwards the request, and writes a row to the spend ledger.
kosha serve # start on :3000
import OpenAI from "openai";
const client = new OpenAI({ baseURL: "http://localhost:3000/proxy/v1", apiKey: "not-used", // kosha resolves credentials from env });
// Use any canonical model ID or alias const res = await client.chat.completions.create({ model: "sonnet", messages: [{ role: "user", content: "hello" }], });
// Let kosha pick the cheapest model you have a key for const cheap = await client.chat.completions.create({ model: "kosha:cheapest", messages: [{ role: "user", content: "hello" }], });
// Cheapest model with tool_use and at least 128k context const routed = await client.chat.completions.create({ model: "kosha:cheapest[tool_use,128k]", messages: [{ role: "user", content: "hello" }], });
`kosha:cheapest` filter syntax (comma-separated, combinable):
| Filter | Example | Meaning |
|---|---|---|
| capability | tool_use, vision | model must have this tag |
k | 128k, 200k | minimum context window |
provider: | provider:groq | pin to a specific provider |
kosha:fastest, kosha:reliable, and kosha:balanced take the same filters and rank on observed latency and circuit-breaker state instead of price.
Every response carries x-kosha-model, x-kosha-provider, x-kosha-requested, x-kosha-attempt-chain, and x-kosha-estimated-cost-usd; non-streaming responses add x-kosha-actual-cost-usd when the upstream returned a usage block.
What the proxy can forward:
| Upstream wire format | Providers | Support |
|---|---|---|
| OpenAI-compatible | OpenAI, Ollama, OpenRouter, Vercel, Groq, Together, Fireworks, DeepInfra, … | passthrough, streaming included |
| Anthropic Messages | Anthropic | translated: streaming, tools, image_url, response_format, reasoning_effort; audio input and non-function tools fail over to an OpenAI-compatible route for the same model |
| Cloud SDKs | Google, Bedrock, Vertex | discovery only, not proxied yet |
| Non-chat / other wire | TypeSafe, Thinking Machines | discovery only — TypeSafe's System One endpoint is not a chat API, and Tinker's Anthropic-wire path differs from Anthropic's own |
Defaults that matter before you expose it: the server binds 127.0.0.1. Pass --host 0.0.0.0 (or KOSHA_HOST) to listen on a network interface, and set KOSHA_PROXY_TOKEN so /proxy/* and POST /api/refresh require Authorization: Bearer or x-kosha-token. KOSHA_MONTHLY_BUDGET_USD caps spend per calendar month. Reference: docs/api.md, docs/operations.md.
kosha-mcp serves the registry over the Model Context Protocol on stdio, so an agent can call kosha_query_models, kosha_cheapest_model, kosha_ranked_routes, kosha_model_detail, kosha_model_routes, kosha_resolve_alias, kosha_provider_health, and kosha_context_strategy without an HTTP server.
claude mcp add kosha -- kosha-mcp
It is published to the MCP registry as io.github.sriinnu/kosha-discovery, so clients that read the registry can install it without a manual command. The manifest is server.json.
Every provider key is optional. With no credentials at all the server still answers from the public models.dev and LiteLLM catalogs plus a curated offline list, so it is useful on a fresh machine.
Tools and protocol details: docs/mcp.md.
46 providers. Each has a descriptor in src/provider-catalog.ts; most OpenAI-compatible ones are driven from GENERIC_OPENAI_PROVIDERS in src/discovery/generic-openai.ts rather than a hand-written class.
| Provider | Discovery | Credential sources |
|---|---|---|
| Anthropic | GET /v1/models (context, output cap, capabilities read from the API) | ANTHROPIC_API_KEY, Claude CLI, Codex CLI |
| OpenAI | GET /v1/models | OPENAI_API_KEY |
GET /v1beta/models | GOOGLE_API_KEY, GEMINI_API_KEY, Gemini CLI, gcloud | |
| AWS Bedrock | SDK → CLI → public catalog → static list | AWS_ACCESS_KEY_ID, ~/.aws/credentials, SSO, IAM |
| Vertex AI | API → gcloud → public catalog → static list | GOOGLE_APPLICATION_CREDENTIALS, ADC |
| Ollama, llama.cpp, LM Studio, vLLM | local HTTP API | none |
| OpenRouter | API | OPENROUTER_API_KEY (optional; unauthenticated is rate-limited) |
| Vercel AI Gateway | GET /v1/models | AI_GATEWAY_API_KEY, VERCEL_OIDC_TOKEN (discovery works without; execution needs one) |
| NVIDIA, Together, Fireworks, Groq, Cerebras, Cohere, DeepInfra, Perplexity | OpenAI-compatible API | _API_KEY |
| DeepSeek, Mistral, Moonshot (Kimi), GLM (Zhipu), Z.AI, MiniMax | OpenAI-compatible API | _API_KEY |
| xAI (Grok) | GET /v1/models; Grok Imagine split into image / video | XAI_API_KEY |
| TypeSafe (System One / Jev) | GET /v1/models — returns judgment models, not chat | TYPESAFE_API_KEY, JEV_API_KEY |
| Thinking Machines (Inkling) | Anthropic-wire endpoint, no model list — public catalog only | TINKER_API_KEY |
| Alibaba Model Studio (Qwen), Volcengine Ark (Doubao), Inception (Mercury), AI21 (Jamba), Upstage (Solar), StepFun, Meta (Muse Spark) | OpenAI-compatible API | DASHSCOPE_API_KEY, ARK_API_KEY, INCEPTION_API_KEY, AI21_API_KEY, UPSTAGE_API_KEY, STEPFUN_API_KEY, META_MODEL_API_KEY |
| Baseten, Nebius Token Factory, Novita AI, SiliconFlow, Hugging Face, Ollama Cloud | OpenAI-compatible API | _API_KEY, HF_TOKEN |
Several providers run separate hosts for international and mainland-China traffic, with separate keys and separate price sheets — Qwen 2.5 72B is $1.40/M input internationally against $0.574/M in China. Merging them would make a model's price depend on which host answered last, so each region is its own provider:
| International | China | Differs in |
|---|---|---|
moonshot (api.moonshot.ai) | moonshot-cn (api.moonshot.cn) | host, key |
minimax (api.minimax.io) | minimax-cn (api.minimaxi.com) | host, key |
alibaba (dashscope-intl) | alibaba-cn (dashscope) | host, key, pricing |
siliconflow (.com) | siliconflow-cn (.cn) | host, key, pricing |
| stepfun (api.stepfun.ai) | stepfun-cn (api.stepfun.com) | host, key, prici
Only the beginning is shown; read the rest in the repo