opencode-mcp

MCP server that lets Claude Code pick an OpenCode model and delegate jobs to the opencode CLI (which must already be installed and authenticated: opencode auth login).

Intended use case

This exists so a Claude Code session can offload work that doesn't need Claude's own reasoning to a cheap/free model instead, without spending Claude tokens on it — code review passes, exploratory bug hunts, well-scoped implementation from a written spec, or any "have someone else look at this" request that doesn't name a specific Anthropic model. It is not a way to run Claude itself more cheaply, and it's not meant for tasks that genuinely need Claude-level reasoning (architecture decisions, ambiguous requirements, anything where a wrong answer is costly) — those should stay on Claude. The tier system (low/mid/high/max, see below) exists specifically so the caller never has to know or care which OpenCode model is currently cheapest-yet-good-enough; it just asks for an intelligence level and gets whatever the data says best fits it today.

Tools

• opencode_check_go_status — confirms the OpenCode Go credential is configured and lists its current model lineup. Pass probe:true to actually ping the mid/high/max tier models (costs a little time/tokens — don't do this routinely).
• opencode_refresh_tiers — recompute the low/mid/high/max tier map from live data (see "Model tiers" below). On-demand only, not routine.
• opencode_start_job — send a prompt/task to a tier (low/mid/high/max) or an explicit model+variant, in a given directory. Returns a jobId immediately (non-blocking).
• opencode_job_status — poll a job; pass waitMs to block until it finishes.
• opencode_list_jobs — list all jobs started this server session.
• opencode_usage_stats — aggregate tokens/cost/response-chars across every job this server has ever delegated (persisted, survives across sessions/processes — unlike opencode_list_jobs). See "Usage tracking" below.
• opencode_cancel_job — kill a running job.
• opencode_list_providers — configured credentials (e.g. OpenCode Zen, OpenCode Go, OpenRouter).
• opencode_list_models — list provider/model ids, optionally filtered by provider. Only needed when a job requires a model outside the tier map.
• opencode_model_info — verbose metadata (cost, context window) for one model.

Jobs shell out to opencode run --format json, parsing its newline-delimited JSON event stream (text, step_finish, error) to assemble the final response text, token usage, and cost as the process runs.

Clean hand-off output, not a transcript

jobSummary(...).text (what opencode_job_status/opencode_start_job return) is built only from type: "text" events — tool calls (file reads, bash, skill loads), step markers, and any reasoning/thinking events are parsed but never included. This is structural (in src/jobs.js's handleEvent), not prompt-dependent, so it holds regardless of style.

On top of that, opencode_start_job's style param (default "handoff") appends a short instruction telling the model to skip preamble/meta-commentary and return just the deliverable — a caller can pass style: "verbose" to get the model's own narration back for debugging (e.g. "why did it read files it didn't need to"). In testing this made a bigger difference on models prone to chatty preambles than on big-pickle, which was already fairly direct — treat it as a nudge, not a guarantee.

Model tiers (src/tiers.js + src/rank.js + src/leaderboard.js)

Callers pick an intelligence tier (low/mid/high/max) instead of memorizing model names or guessing which one is actually good. The map is data-driven, built by computeTierMap() (src/rank.js):

1. Pull every opencode-go/* model's real per-token cost from opencode models opencode-go --verbose (local, authoritative — no scraping needed for this axis; the Go plan's advertised "requests per week" chart is just this cost data divided into a dollar budget, confirmed by cross-check).
2. Scrape arena.ai/leaderboard/code/webdev (src/leaderboard.js, plain server-rendered HTML table, no JS execution needed) for each model's WebDev/code score — matching handles the leaderboard's reasoning-effort suffixes (-max, -high, -xhigh, dated snapshots), trying an exact id match first so a real distinct SKU like qwen3.8-max isn't mistaken for qwen3.8 at variant max.
3. Sort all matched models by cost ascending and compute a cost ceiling per tier from the quartile cutoffs (low's ceiling = 25th-percentile cost, mid's = 50th, high's = 75th, max has none). Each tier's pool is cumulative — every candidate at or under its ceiling, not just the ones in its own quartile — and its winner is the single highest-arena-score model in that pool. This means a cheap model that outperforms everything pricier below its ceiling wins every tier up to that ceiling: observed 2026-08-03, gpt-5.6-luna (cheap enough for low) outscored every model in the mid bracket and won both — there's no reason to pay more for something worse. Pools nest (low ⊆ mid ⊆ high ⊆ max) so scores never decrease going up the tiers.
4. Live-probe each tier's top pick with a trivial prompt before saving; if it's unreachable (e.g. region-locked — observed with deepseek-v4-flash, which otherwise would have won low+mid), fall through to the next-best candidate in the same pool instead of saving a pick that would silently fail every job. A tier that exhausts its whole pool without success still gets saved (best-effort) but flagged verified: false.
5. Flag a tier inherited: true (with inheritedFrom: "<cheaper tier>") when its final winner is the same model+variant as a cheaper tier's — i.e. the collapse in step 3 actually happened, post-validation. This is informational only; it doesn't change any selection.
6. Persist the result to tiers.generated.json (gitignored — regenerate, don't hand-edit) so opencode_start_job reads it with zero extra latency/network.

This refresh happens automatically, at most once a day, with no tool call and no tokens spent describing it — opencode_start_job (and opencode_check_go_status, and server startup) check tiers.isStale() and fire the refresh in the background (fire-and-forget) if the saved map is missing or >24h old. The job that triggered the check still runs against whatever's on disk right now; the refreshed map is ready for the next call. opencode_refresh_tiers still exists as a manual override for "I need this recomputed right now," not for routine use.

Models the leaderboard has no entry for (e.g. qwen3.7-plus as of 2026-08) are excluded from tiers but reported in unmatched, never silently dropped.

Token/cost discipline: default to low unless the task clearly needs more reasoning depth — mid/high/max don't cost extra dollars under a Go subscription, but every job still burns real tokens and wall-clock time. An explicit model (+ optional variant) param on opencode_start_job overrides the tier for one-off cases outside the map.

Notable free/no-extra-cost models seen on this machine

• opencode/big-pickle — free on OpenCode Zen (cost: 0).
• opencode-go/* — included in the OpenCode Go subscription. Some entries can be slow, region-restricted, or hang depending on OpenCode's backend that day — opencode_cancel_job exists for exactly that.

Usage tracking (src/usage.js)

Every job, on completion (success or failure), appends one line to ~/.local/share/opencode-mcp/usage-log.jsonl — outside the repo, machine-local, grows forever, same convention as opencode's own ~/.local/share/opencode. Each record has tokens, list-price cost, prompt/response character counts, tier, model, and duration. opencode_usage_stats reads it back and aggregates totals + a per-model breakdown; pass sinceHours to scope to recent activity only.

cost is OpenCode's own list price for the tokens used — under the Go subscription (flat-rate) or Zen (free tier) the dollars actually charged is $0 regardless, so the aggregated total is the savings from delegating instead of paying per-token. It is not a comparison to Claude/Anthropic API pricing — there's no reliable way to know what equivalent work would have cost in a different model's tokenizer, so this tool doesn't claim to measure that.

This log is separate from (and complements) OpenCode's own opencode stats --models, which aggregates all opencode usage on the machine regardless of what started it — use that for the full-machine picture, use opencode_usage_stats to scope specifically to what this MCP server delegated.

Install

npm install

Register with Claude Code

claude mcp add opencode --scope user -- node /path/to/opencode-mcp/src/index.js

Replace /path/to/opencode-mcp with wherever you cloned this repo (e.g. run pwd from inside it to get the absolute path).

Takes effect in new Claude Code sessions (an already-running session won't pick up newly registered servers).

Safety note

opencode_start_job accepts an auto flag that maps to opencode's --auto (auto-approve all tool permissions). It's off by default; only set it for jobs you trust to edit files / run commands unattended.