Cap Shield MCP server

CAP-Shield MCP server

Context selection and compression for AI agents — with the recall measured, not claimed.

One file. No dependencies. No SDK required.

Why

More context makes agents worse. ETH Zurich found context files LOWER task success versus giving the agent no repository context at all, while raising inference cost by over 20 %. Around two thirds of production agent failures trace to context problems, not to the model being incapable.

So the question is not how much you cut. It is whether what you kept was enough — and that is measured here, on a benchmark we did not choose: LongMemEval-S, 500 questions. Recall@10 of 93.8 % against a lexical baseline of 51.9 %.

Recall@10 is the strict measure: a question counts only when ALL gold sessions were found. Finding half the answer means the agent answers confidently on half a basis.

Two of the six tools need no account. Measure first, decide after.

Install

curl -O https://cap-shield-robin.fly.dev/cap_mcp.py

{
  "mcpServers": {
    "cap-shield": {
      "command": "python",
      "args": ["/absolute/path/to/cap_mcp.py"]
    }
  }
}

Python 3.9+. Nothing else.

The optional SKILL.md tells an agent when to use these tools — and when not to.

Tools

measuretraffic · no account*

Measure how much of your own agent traffic could be saved. NO ACCOUNT OR KEY NEEDED — use this first. Returns byte savings over the wire and, if a query is given, token savings from selective context retrieval. Nothing is stored: the text is compressed in memory and discarded. Rate limited to 20 calls per hour per IP.

listpackages · no account*

List the available dictionaries with their MEASURED compression, including the ones that perform badly. Each entry says whether it works one message at a time or only batched, and how many messages came out LARGER. No key needed.

remember · requires a key

Store a memory entry for later retrieval. REQUIRES A KEY. This does not call any language model — it stores text in an isolated per-tenant archive. Use assemble_context to get relevant entries back.

assemblecontext · requires a key*

Retrieve the memory entries that answer a question, within a token budget. REQUIRES A KEY. Send the returned 'context' to your language model INSTEAD of the whole history. This does not call a model itself — it selects what to send. The budget is a ceiling, not a target: selection stops where relevance runs out, often well below it. The response says how many entries were left behind and why.

get_account

Get an account and an API key. Requires an email address. The key is returned ONCE and cannot be shown again — store it immediately. Beta quotas are low by design; they are hard stops, never overage billing.

issuepass · requires a key*

Issue a short-lived (15 minute) signed credential (a JWT) stating who the caller acts for. REQUIRES A KEY. Anyone can verify it independently against /.well-known/jwks.json — without calling CAP-Shield again. 'acts_for' and 'scope' are NOT validated against reality: the pass only attests that the key holder claimed this, at this time, and that the claim is recorded in the audit chain. This is identity and claimed authorization, not a payment or access-control mechanism.

The descriptions above are copied verbatim from the server. If they ever differ from what tools/list returns, the server is right and this file is stale.

What the numbers mean

Compression saves bytes over the wire. Selection saves tokens in the context. Two different mechanisms — adding them together produces a number that means nothing.

Compressed packets are decompressed before a model sees them, so this does not reduce inference cost. Saying otherwise is the easiest way to be wrong about this project.

Every figure is published live, including what has not been measured and which packages perform badly:

Shortened. The full README is on GitHub.