Session-scoped authorization for AI agents. Stops the attack where every call is legitimate and the sequence is not.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
claude mcp add clayseal-capabilities -- npx -y @your-org/mcp-server{
"mcpServers": {
"clayseal-capabilities": {
"command": "npx",
"args": ["-y", "@your-org/mcp-server"]
}
}
}MCP Servers overview
# Clay Seal
<!-- mcp-name: io.github.clayseal/clayseal -->
<img src="https://raw.githubusercontent.com/clayseal/clayseal-capabilities/main/docs/assets/clay-seal-logo.png" alt="Clay Seal logo" width="420">
[](LICENSE)
[](pyproject.toml)
[](.github/workflows/ci.yml)
[](https://pypi.org/project/clayseal/)
**Incorporating business-process-logic constraints into AI systems.**
Your agent has a $1,000 refund ceiling. It issues eleven refunds of $900. Every
call is inside the per-refund limit, so every per-call check passes, and $9,900
goes out the door. Nothing that looks at one call at a time can see this.
Clay Seal sits in front of your tools and judges each call against the whole
session: the grant, the running totals, where the arguments came from, and what
the agent has already done.
## Contents
| | |
| --- | --- |
| [Start here](#start-here) | install and watch it stop two attacks |
| [Use it in two lines](#use-it-in-two-lines) | wrap tools you already have |
| [In front of an MCP server](#or-put-it-in-front-of-an-mcp-server) | no code change |
| [Write the policy](#write-the-policy) | the grant an operator seals |
| [How it works](#how-it-works) | the six layers, in the order they run |
| [What it measures](#what-it-measures) | the numbers, and what they are not |
| [Where the boundary is](#where-the-boundary-is) | what this does not do |
| [Documentation](#documentation) | [docs/START.md](https://github.com/clayseal/clayseal-capabilities/blob/main/docs/START.md) first, then [docs/](https://github.com/clayseal/clayseal-capabilities/blob/main/docs/README.md) |
| [Build from source](#build-from-source) | and run the tests |
**Working on the code?** [docs/INTEGRATION.md](https://github.com/clayseal/clayseal-capabilities/blob/main/docs/INTEGRATION.md) names which
modules the shipped gateway actually executes, which is the fastest way to tell
the live path from the parts that are not wired in. The decision itself is
`SessionBroker._authorize_locked` in
[clayseal/capabilities/broker.py](https://github.com/clayseal/clayseal-capabilities/blob/main/clayseal/capabilities/broker.py); most refusals
come from `_floor` in the same file.
## Start here
If you are a coding agent, [skip to the deploy runbook](#if-you-are-an-agent).
```bash
pip install clayseal
clayseal try
```
`clayseal try` takes about a minute. It runs two attacks in front of you and
shows the gateway stopping them. There is nothing to configure, no key to get
and nothing leaves your machine.
<img src="https://raw.githubusercontent.com/clayseal/clayseal-capabilities/main/docs/assets/clayseal-try.svg" alt="clayseal try: eleven $900 refunds against a $1,000 ceiling, the first allowed and the rest refused, then an injected email recipient held for a person" width="820">
Every verdict there is decided live by the same gateway you would deploy. The
picture is generated from a real run by `python scripts/render_try_svg.py`, and
a test fails if it drifts from what the command prints, so it cannot become a
screenshot of something that used to work.
Python 3.10 to 3.14. Two dependencies, `cryptography` and `pyyaml`.
> **On names.** The directory is `agentauth-capabilities`, the package is
> `clayseal`, and the import is `clayseal.capabilities`. The project was renamed
> at 0.6; `agentauth` still imports as a deprecation shim. See
> [Naming](#naming) at the end for the detail.
## If you are an agent
Do this in the project you are editing, in order:
```bash
clayseal try --fast
clayseal howto
clayseal policy new > policy.yaml
# rename your_* tools; put mailboxes in egress.recipients; then:
clayseal policy lint policy.yaml
clayseal skill --write
```
Python functions: `Guardrail.from_policy_file("policy.yaml").wrap_all({...})`
and bind the wrappers. Keys must match `tools.allow`. Construct one
`Guardrail` per session (a new one resets ceilings). Catch `Refused`
(budget / not granted) and `StepUpRequired` (held — typical for off-list
email). Non-file tools go in `paths.pathless`.
Already an MCP server (Claude Desktop / Cursor): `clayseal proxy`, not
`clayseal serve`. Do not point `proxy` at a plain `.py` file. `clayseal howto`
is the runbook.
## Use it in two lines
Wrap the tools you already have. Nothing else about your agent changes.
```python
from clayseal.capabilities import Guardrail, Refused, StepUpRequired
def list_open_refunds():
return [{"invoice": "INV-001", "amount": 900.0},
{"invoice": "INV-002", "amount": 900.0}]
def issue_refund(invoice, amount):
return f"refunded {invoice} ${amount:.2f}"
guard = Guardrail.from_dict({
"version": 1,
"goal": {"id": "refund-run",
"summary": "Refund the invoices the customer disputed."},
"expires_at": "2030-01-01T00:00:00Z",
"tools": {"allow": ["list_open_refunds", "issue_refund"],
"harmless": ["list_open_refunds"],
"effects": {"list_open_refunds": "read", "issue_refund": "transfer"}},
"paths": {"pathless": ["list_open_refunds", "issue_refund"]},
"budgets": {"value": {"ceilings": {"refunds": "1000.00"},
"tracked": {"issue_refund": {"arg": "amount",
"budget": "refunds"}}}},
})
tools = guard.wrap_all({"list_open_refunds": list_open_refunds,
"issue_refund": issue_refund})
# Call them exactly as before. The gateway decides before the tool runs.
for row in tools["list_open_refunds"]():
try:
print(tools["issue_refund"](invoice=row["invoice"], amount=row["amount"]))
except Refused as exc:
print("refused:", exc.reasons) # hand the reason back to the agent
except StepUpRequired as exc:
print("needs a human:", exc.reasons)
```
The first refund goes through. The second is refused, because the $1,000
session ceiling is already spent. Neither call reached your function.
The policy is inline here so you can paste the whole thing into a file and run
it. In a real deployment it lives in its own YAML, where a security team can
review it and a pull request can gate it. `clayseal policy new > policy.yaml`
writes a commented one to start from, and `Guardrail.from_policy_file` loads it.
The wrappers keep the name, docstring and signature of your originals, so any
framework that introspects them sees the tool it saw before. That covers
LangGraph, the OpenAI Agents SDK, CrewAI and hand-written loops. Call the
wrappers the same way you called the originals, positionally or by name.
## Or put it in front of an MCP server
No code change at all. The gateway speaks MCP, so it sits between your agent and
the server:
```bash
clayseal proxy --policy policy.yaml -- npx @your-org/mcp-server
```
Or paste this into Claude Desktop's MCP config, Cursor's MCP settings, or
`.cursor/mcp.json`. The agent talks to Clay Seal; Clay Seal talks to the server:
```json
{
"mcpServers": {
"billing": {
"command": "clayseal",
"args": ["proxy", "--policy", "policy.yaml",
"--", "npx", "@your-org/mcp-server"]
}
}
}
```
Tools the policy does not grant are removed from the catalogue, so the agent is
never told they exist.
## Write the policy
This is a whole policy. It lints clean.
```yaml
version: 1
goal:
id: refund-run
summary: Refund the invoices the customer disputed.
expires_at: 2027-12-31T00:00:00Z
tools:
allow: [list_open_refunds, issue_refund]
harmless: [list_open_refunds] # a read spends nothing
effects: {list_open_refunds: read, issue_refund: transfer}
paths:
pathless: [list_open_refunds, issue_refund] # these act on invoices, not files
egress:
domains: [acme.example]
recipients: [ops@acme.example] # a domain alone is every mailbox
bind_recipients: true
budgets:
value:
ceilings: {refunds: "1000.00"} # dollars, for the whole session
tracked:
issue_refund: {arg: amount, budget: refunds}
```
Run `clayseal policy lint policy.yaml` before you ship. It catches the mistake
that matters most: a tool that can spend money but debits no budget. On the file
above it reports no errors and one warning (the ceiling is per session unless
you bind a principal), which names a real decision you have not made yet.
Full reference: [docs/POLICY.md](https://github.com/clayseal/clayseal-capabilities/blob/main/docs/POLICY.md).
## See it stop the attack
From a checkout. [examples/](https://github.com/clayseal/clayseal-capabilities/blob/main/examples/) has seven, each runnable with no key and
no network:
```bash
python examples/02_the_proxy.py
```
```
tools advertised to the agent: list_open_refunds, issue_refund
(the server offers wire_funds; the policy does not grant it, so the agent is never told it exists)
ok issue_refund INV-001 paid INV-001 $900.00
DENY issue_refund INV-002 refused by policy: value_budget_exceeded
...
DENY issue_refund INV-011 refused by policy: value_budget_exceeded
DENY wire_funds ops-float refused by policy: tool 'wire_funds' is not in this session's policy
the server executed 2 call(s):
list_open_refunds {}
issue_refund {"amount": 900.0, "invoice": "INV-001"}
clayseal proxy: 2 allowed, 11 denied, 0 held for approval; withheld from the catalog: wire_funds
```
Read the last block. A JSON-RPC error proves the agent was told
no; the server's own ledger proves the refund did not happen, and those are
different claims. The same run on the command line, which is the deployment
shape a deployment actually uses:
```bash
clayseal proxy --policy examples/refund.yaml -- python examples/refund_server.py
```
## How it works
You give the gateway a **policy fiWhat people ask about clayseal-capabilities
What is clayseal/clayseal-capabilities?
+
clayseal/clayseal-capabilities is mcp servers for the Claude AI ecosystem. Session-scoped authorization for AI agents. Stops the attack where every call is legitimate and the sequence is not. It has 1 GitHub stars and its last recorded update is dated 2026-09-08.
How do I install clayseal-capabilities?
+
You can install clayseal-capabilities by cloning the repository (https://github.com/clayseal/clayseal-capabilities) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is clayseal/clayseal-capabilities safe to use?
+
Our security agent has analyzed clayseal/clayseal-capabilities and assigned a Trust Score of 95/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains clayseal/clayseal-capabilities?
+
clayseal/clayseal-capabilities is maintained by clayseal. The last recorded GitHub activity is dated 2026-09-08, with 0 open issues.
Are there alternatives to clayseal-capabilities?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
Deploy clayseal-capabilities to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/clayseal-clayseal-capabilities)<a href="https://claudewave.com/repo/clayseal-clayseal-capabilities"><img src="https://claudewave.com/api/badge/clayseal-clayseal-capabilities" alt="Featured on ClaudeWave: clayseal/clayseal-capabilities" width="320" height="64" /></a>More MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
The fastest path to AI-powered full stack observability, even for lean teams.
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!