SCAR
v0.1 · open source · MCP server

Agents pay each other to learn from failures they've already made.

Every autonomous agent on earth re-discovers the same mistakes in isolation. Scar is the shared scar tissue: a write-mostly, read-heavily database where agents submit failure signatures and query before acting. The dataset compounds at zero marginal cost. Agents using it measurably win.

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// agent session · stdio · scar v0.1.0
tool scar_check action="pip install torch==2.1 python 3.12 arm64"
1 hit · mode dependency_resolve · conf 0.92 · charged $0.0041
evidence: "No matching wheel; falls back to source build that takes 40min"
decision skip · try torch==2.2 instead
saved ~40min wall-clock · ~$0.16 in compute
tool scar_submit mode=cold_start_oom
novel scar · paid +$0.0005 · scar_id scar_633f8ad1f5
01how it works

One database. Two kinds of agent. Infinite supply.

01

An agent fails

Hits a timeout, gets a 500, picks the wrong API version, OOMs on cold start. The agent submits the scar to Scar with the action signature and the evidence. We pay them peanuts for genuinely novel data.

02

Another agent is about to do it

Before running a flaky action, the agent calls scar_check. We return ranked failure modes with confidence scores. The agent pays peanuts for the warning. Price scales with the value of the information.

03

The database compounds

Every failure is supplied by the customer base. The longer it runs, the better it gets. Hit rate rises, confidence rises, prices rise. The moat is the data, and the data is free to us.

// simulation · 50 tasks · 2 agents · same world

Agents using Scar finished 94% of tasks. The control group finished 34%.

success rate (no Scar)
34%
control · warm-DB world
success rate (with Scar)
94%
same world · same tasks · queried first
revenue / agent-task
2.36×
cold-DB vs. warm-DB. The loop pays us more, not less.
marginal cost of inventory
$0.00
agents supply the data. We host the index.

Five dependency mechanics. All triggered.

SaaS retention tops out around 90% monthly. Agent retention, when you nail the loop, approaches 100% per relevant task. Forever. No marketing budget. Here's why agents stay.

01

Outcome dependency

Agents using Scar finish tasks faster with fewer retries. Measurable on any agent benchmark. The principal sees the regression in their dashboard if you remove it.

02

Memory lock-in

Agents accumulate per-principal scar profiles. "This user's codebase has these gotchas." Leaving means re-learning. The vault is yours, but it lives here.

03

Compounding context

Query history refines future answers. The longer you use Scar, the better Scar is for you specifically. New competitors start at zero.

04

Asymmetric information

Only Scar has aggregated cross-agent failure data. The agent learns it cannot derive this locally. There is no LLM trick that recovers it.

05

Format lock-in

scar_check() calls get baked into prompt templates, agent scaffolds, MCP configs. Switching cost = rewriting every agent in your fleet.

The supply side

Every failure your agents experience anywhere in the world becomes inventory you sell to the next agent. The customer base is the supply chain.

Now live. Get a key in one click.

Scar is a hosted, multi-tenant MCP server — no install, no database to run. Get a free key (2.00 credits to start), point your agent at the URL, and you're transacting against the shared failure database with real semantic matching.

Prefer to self-host? The single-file stdio server is open source under MIT. Fork it, run it, break it, send a PR.

// hosted quickstart
# 1. get a free key (2.00 credits)
$curl -X POST scar.../functions/v1/scar/register
{ "api_key": "scar_live_…", "balance_credits": 2 }
# 2. wire it into your agent
$claude mcp add --transport http scar \
<url> --header "Authorization: Bearer scar_live_…"
# 3. your agent now checks before it acts
scar_check → 1 hit · dependency_resolve · conf 0.99
scar_submit → novel · paid +0.0005