Kosha Model Discovery

io.github.sriinnuPublished by GitHub account sriinnuv1.13.0Updated Nov 4, 2026

Tells your agent which model to use and what it costs, across 46 providers and local runtimes.

The publisher's own server: model.tools — Zero-Auth Agent Utilities

VerifiedSTDIODesktop onlyDeveloper ToolsAI & ML

Overview

AI-generated overview

Serves a model catalog over MCP so an assistant can compare models, prices, context limits and routes across 46 providers and local runtimes.

What you can do
  • Query and filter the model registry across providers, modes and capabilities
  • Rank models by price, or find the cheapest model matching capability and context requirements
  • Inspect a single model's pricing, limits and lifecycle status, and list every route or region serving it
  • Resolve short aliases to canonical model IDs and check provider health
  • Suggest a context strategy for a given model
When to use it
Useful when an assistant has to choose between models, compare prices and context windows, or check whether a pinned model is deprecated or still served. It also helps on a fresh machine, since it answers from public catalogs without any API keys.
Requirements
Runs locally over stdio as the npm package @sriinnu/kosha-discovery (kosha-mcp) and needs Node.js 22+. All provider API keys are optional; without them it falls back to public catalogs and a bundled list. Optional variables include ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, OPENROUTER_API_KEY, XAI_API_KEY, TYPESAFE_API_KEY and KOSHA_CACHE_TTL_HOURS.
Before you install
It reads provider credentials that already exist on the machine, including environment variables, CLI config files, gcloud ADC and AWS SSO, so it can see more than the keys you set for it. Discovery results are cached under the user's home directory. The MCP server itself only reads catalog data; the separate proxy and HTTP API, which can forward paid requests, are not part of it.

Installation

In SourceWeft

  1. Open Kosha Model Discovery in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

[Kosha — AI Model Discovery]

kosha-discovery

Tells your agent — or your code — which model to use and what it costs.

Website: kosha.srinivas.dev

[Sponsor] [Buy Me a Coffee]

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.

Install

bash
npm install @sriinnu/kosha-discovery       # library / servernpm install -g @sriinnu/kosha-discovery    # global `kosha` CLIbrew install sriinnu/tap/kosha             # same CLI and MCP server, via Homebrew

Requires Node.js 22+.

Quick start

Library

typescript
import { createKosha } from "@sriinnu/kosha-discovery";
const kosha = await createKosha();
const models   = kosha.models();                          // ModelCard[] across every providerconst cheapest = kosha.cheapestModels({ role: "image" }); // ranked by price, with missingCredentialsconst sonnet   = kosha.model("sonnet");                   // alias → canonical ID; undefined if unknownconsole.log(sonnet?.pricing); // { inputPerMillion: 2, outputPerMillion: 10, cacheReadPerMillion: 0.2, ... }

CLI

bash
kosha discover                       # query every provider; writes ~/.kosha/cache and the manifestkosha list --provider anthropic      # read from the local cachekosha model sonnet                   # one model, alias-awarekosha routes claude-opus-5-5         # every serving route for a model (direct, OpenRouter, Bedrock, …)kosha cheapest --role embeddings     # rank by price for a rolekosha doctor --ci --model opus,gpt6  # fail CI if a model you pin is deprecated, retiring, or gonekosha spend --since 2026-09-01       # roll up the proxy's spend ledgerkosha refresh                        # bypass the cache and re-discoverkosha 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:

bash
jq '.models[] | select(.pricing.inputPerMillion < 0.1) | .modelId' ~/.kosha/registry.json

GitHub Action

Fail a build when a model your code pins is deprecated, about to be retired, or listed by no provider any more:

yaml
- 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.

Public snapshot

A weekly discovery run publishes a full snapshot — every provider, model, price and limit kosha can see without your keys — at a stable URL:

bash
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.

HTTP API

GET  /api/models?provider=&originProvider=&mode=&capability=GET  /api/models/:idOrAlias             GET  /api/models/:idOrAlias/routesGET  /api/models/cheapest?role=…        GET  /api/capabilitiesGET  /api/providers[/:id]               GET  /api/rolesGET  /api/resolve/:alias                GET  /api/discovery-errorsGET  /api/discovery[/delta|/watch|/cheapest|/binding]   (stable v1 contract)POST /api/refresh                       GET  /health          GET  /metricsGET  /proxy/v1/models                   POST /proxy/v1/chat/completions

Parameters and response shapes: docs/api.md.

Proxy

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.

bash
kosha serve   # start on :3000
typescript
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 aliasconst res = await client.chat.completions.create({  model: "sonnet",  messages: [{ role: "user", content: "hello" }],});
// Let kosha pick the cheapest model you have a key forconst cheap = await client.chat.completions.create({  model: "kosha:cheapest",  messages: [{ role: "user", content: "hello" }],});
// Cheapest model with tool_use and at least 128k contextconst routed = await client.chat.completions.create({  model: "kosha:cheapest[tool_use,128k]",  messages: [{ role: "user", content: "hello" }],});

kosha:cheapest filter syntax (comma-separated, combinable):

FilterExampleMeaning
capabilitytool_use, visionmodel must have this tag
<N>k128k, 200kminimum context window
provider:<id>provider:groqpin 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 formatProvidersSupport
OpenAI-compatibleOpenAI, Ollama, OpenRouter, Vercel, Groq, Together, Fireworks, DeepInfra, …passthrough, streaming included
Anthropic MessagesAnthropictranslated: 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 SDKsGoogle, Bedrock, Vertexdiscovery only, not proxied yet
Non-chat / other wireTypeSafe, Thinking Machinesdiscovery 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 <token> or x-kosha-token. KOSHA_MONTHLY_BUDGET_USD caps spend per calendar month. Reference: docs/api.md, docs/operations.md.

MCP server

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.

bash
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.

Supported providers

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.

ProviderDiscoveryCredential sources
AnthropicGET /v1/models (context, output cap, capabilities read from the API)ANTHROPIC_API_KEY, Claude CLI, Codex CLI
OpenAIGET /v1/modelsOPENAI_API_KEY
GoogleGET /v1beta/modelsGOOGLE_API_KEY, GEMINI_API_KEY, Gemini CLI, gcloud
AWS BedrockSDK → CLI → public catalog → static listAWS_ACCESS_KEY_ID, ~/.aws/credentials, SSO, IAM
Vertex AIAPI → gcloud → public catalog → static listGOOGLE_APPLICATION_CREDENTIALS, ADC
Ollama, llama.cpp, LM Studio, vLLMlocal HTTP APInone
OpenRouterAPIOPENROUTER_API_KEY (optional; unauthenticated is rate-limited)
Vercel AI GatewayGET /v1/modelsAI_GATEWAY_API_KEY, VERCEL_OIDC_TOKEN (discovery works without; execution needs one)
NVIDIA, Together, Fireworks, Groq, Cerebras, Cohere, DeepInfra, PerplexityOpenAI-compatible API<PROVIDER>_API_KEY
DeepSeek, Mistral, Moonshot (Kimi), GLM (Zhipu), Z.AI, MiniMaxOpenAI-compatible API<PROVIDER>_API_KEY
xAI (Grok)GET /v1/models; Grok Imagine split into image / videoXAI_API_KEY
TypeSafe (System One / Jev)GET /v1/models — returns judgment models, not chatTYPESAFE_API_KEY, JEV_API_KEY
Thinking Machines (Inkling)Anthropic-wire endpoint, no model list — public catalog onlyTINKER_API_KEY
Alibaba Model Studio (Qwen), Volcengine Ark (Doubao), Inception (Mercury), AI21 (Jamba), Upstage (Solar), StepFun, Meta (Muse Spark)OpenAI-compatible APIDASHSCOPE_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 CloudOpenAI-compatible API<PROVIDER>_API_KEY, HF_TOKEN

Regional pairs

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:

InternationalChinaDiffers 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, pricing
zai (api.z.ai)glm (open.bigmodel.cn)host, key, pricing

kosha routes <model> lists every region a model is served from, so you can compare prices across them directly.

Without a key, providers fall back to the public models.dev + LiteLLM catalog, then to kosha's own weekly snapshot if those are unreachable, then to a curated static list, so kosha list works on a fresh machine. The same fallback applies when a key is present but rejected: the provider stays listed from the public catalog, shows as unauthenticated, and the error is kept for kosha doctor. Exact env var names: docs/credentials.md.

To take models.dev and LiteLLM out of the loop, set KOSHA_CATALOG_SOURCE=pinned (kosha's snapshot only) or local (a file from kosha registry export, no network). See Catalog Source.

Non-chat modes

Most models are chat, but mode also covers embedding, image, video, audio, moderation, rerank, and judgment. A judgment model — TypeSafe's System One family — answers a question with a typed value (a choice and its probability distribution, a probability, or a score on described levels) instead of generating text. It carries no chat capability on purpose: routing a prompt to one would be a category error.

bash
kosha cheapest --role judgment    # rank judgment models by pricekosha model jev                   # mode: judgment, $0.042/M in, $0 out

How it works

  1. Discovery — one discoverer per provider runs concurrently (Promise.allSettled); each returns normalized ModelCards. A failing provider is recorded in discoveryErrors() and doesn't block the others.
  2. Enrichment — pricing, context window, and output cap are filled from models.dev and LiteLLM where the provider API doesn't publish them; pricingSource on each card says which. Cards also carry releaseDate and a lifecycle status where a catalog publishes them.
  3. Resilience — a per-provider circuit breaker with exponential cooldown, plus stale-cache fallback, so a provider outage degrades to cached data rather than an error.
  4. Cache and manifest — results are cached under ~/.kosha/cache/ (24 h TTL) and exported as a versioned snapshot at ~/.kosha/registry.json for other tools to read.
  5. Proxy — resolves the requested model or kosha:<strategy>[filters] selector against the registry, ranks candidate routes, forwards with failover, and records estimated and reconciled cost in ~/.kosha/ledger-YYYY-MM.jsonl.

Details: docs/architecture.md, docs/resilience.md.

Development

bash
pnpm installpnpm run build        # compile to dist/pnpm run typecheck    # tsc --noEmitpnpm run lint         # biome lintpnpm test             # vitest runpnpm run check        # lint + build + test
pnpm aliases:check snapshot.json        # built-in aliases that are dead or a generation behindpnpm snapshot:diff old.json new.json    # what changed between two snapshots

The alias table in src/aliases.ts is the one part of the catalog written by hand. aliases:check compares it against a snapshot, and the weekly snapshot run annotates itself with the result, so a bare alias like opus falling behind shows up there rather than in a bug report.

Project layout

src/  cli.ts                 # CLI entry point and arg parsing  cli-commands.ts        # command implementations (+ cli-cmd-*.ts for the larger ones)  registry.ts            # ModelRegistry public API  registry-runtime.ts    # discovery orchestration, enrichment, cache, manifest export  registry-query.ts      # model / role / capability queries  registry-selection.ts  # cheapest candidates, binding hints  registry-routing.ts    # cheapest / fastest / reliable / balanced ranking  discovery/             # one discoverer per provider + static and public-seed catalogs  enrichment/            # models.dev + LiteLLM pricing enrichment  credentials/           # credential resolution (env, CLI files, ADC, OAuth)  provider-catalog.ts    # provider descriptors: base URLs, transport, env var names  aliases.ts             # built-in short names → canonical model IDs  claude-generation.ts   # which Claude generation accepts which API parameters  model-features.ts      # tool dialect / structured-output inference per model  proxy.ts               # OpenAI-compatible proxy: routing, failover, ledger  wire-anthropic.ts      # OpenAI ↔ Anthropic request / response / SSE translation  cost.ts                # spend ledger, budget gates, usage reconciliation  tally.ts               # zero-dependency token usage + USD tally (also exported as ./tally)  server.ts              # Hono HTTP API + operator token gate  mcp-server.ts          # MCP stdio server  types.ts               # shared types

Adding a provider

For an OpenAI-compatible provider — most of them — it is two table entries:

  1. Add a descriptor to PROVIDER_CATALOG in src/provider-catalog.ts (id, base URL, transport, credential env vars).
  2. Add a spec to GENERIC_OPENAI_PROVIDERS in src/discovery/generic-openai.ts. It registers its own discoverer, resolves credentials from the descriptor's credentialEnvVars, and falls back to the public catalog when there is no key.
  3. Map the provider to its models.dev / LiteLLM slug in src/discovery/modelsdev-seed.ts and litellm-seed.ts so keyless discovery works. Check the slug — GLM is published as zhipuai, and a missing mapping means no keyless models at all.
  4. Add a case to test/discovery/generic-openai.test.ts and document the env vars in docs/credentials.md.

Write a discoverer class only when classification genuinely needs code — per-route pricing (OpenRouter), a non-OpenAI response envelope (TypeSafe), or origin remapping (Vercel):

  1. Create src/discovery/<provider>.ts extending BaseDiscoverer or OpenAICompatibleDiscoverer; fetchJSON and makeCard are provided.
  2. Export it from src/discovery/index.ts and add a factory entry to DISCOVERER_REGISTRY.
  3. Add a resolver branch in src/credentials/resolver.ts only for a bespoke search order (CLI files, OAuth, ADC, SSO). A plain API key in env vars needs no branch — the descriptor covers it.
  4. Add test/discovery/<provider>.test.ts (mock fetch; see typesafe.test.ts).

Docs

CredentialsEnv vars, CLI tools, and config files for every provider
CLICommands, flags, examples
HTTP APIEndpoints, parameters, response schemas
MCP serverTools, protocol negotiation, client setup
ConfigurationAliases, routing, enrichment, programmatic config
ArchitectureDiscovery flow, module map, adding providers
ResilienceCircuit breakers, stale cache, health
OperationsDeployment sizing, metrics, spend ledger, recovery recipes
SecurityThreat catalogue, runtime scanning, pre-commit hook
Discovery Plane v1Stable daemon contract (deltas, SSE watch, binding hints)

The same pages are mirrored to the wiki, alongside guides that only live there (troubleshooting, the release runbook). docs/ is the source: the mirror is regenerated on every push to main, so edit here, not there.

Release

  1. Bump version in package.json and server.json (both places), and date the [Unreleased] section in CHANGELOG.md; merge that as a PR.
  2. Tag and push:
bash
git tag -s vX.Y.Z -m "vX.Y.Z" && git push origin vX.Y.Zgh workflow run release-npm.yml --ref vX.Y.Z -f tag=vX.Y.Z

The workflow checks that the tag matches package.json, runs lint / build / test, stages the package on npm with provenance, and creates the GitHub Release. Staging authenticates through npm trusted publishing (OIDC); there is no npm token. The version goes live only after a maintainer approves it with 2FA:

bash
npm stage list @sriinnu/kosha-discoverynpm stage approve <id>gh workflow run release-npm.yml --ref vX.Y.Z -f tag=vX.Y.Z -f publish_npm=false -f create_github_release=false   # then publish to the MCP registry

Support

kosha is free and open source. If it helps you pick cheaper models, consider sponsoring its upkeep:

License

MIT

Source: README.md at commit 14c7454

Tools

0
SourceWeft started this package once in an isolated sandbox, without credentials, to list its tools. That failed: the server did not start.

Version history

8
  1. v1.13.0LatestNov 4, 2026
  2. v1.12.1Nov 4, 2026
  3. v1.12.0Nov 3, 2026
  4. v1.11.0Nov 1, 2026
  5. v1.9.1Oct 29, 2026
  6. v1.8.1Oct 6, 2026
  7. v1.7.0Oct 5, 2026
  8. v1.6.2Sep 19, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

Other Model integrations