Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Tells your agent which model to use and what it costs, across 46 providers and local runtimes.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, OPENROUTER_API_KEY, XAI_API_KEY, TYPESAFE_API_KEY, KOSHA_CACHE_TTL_HOURS标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 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"
}
}
}
}在终端中运行这条命令
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-discovery运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
KOSHA_CACHE_TTL_HOURS env这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
注册名称已通过 GitHub 账户 sriinnu 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, OPENROUTER_API_KEY, XAI_API_KEY, TYPESAFE_API_KEY
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.13.0)于4天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Spec Driven Development, Remote Desktop Commander, NexusWindows
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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.
Windows computer use in the background: desktop apps driven without your mouse or keyboard.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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
只显示了开头部分;其余内容请在仓库中阅读