network_tls_fingerprint
Compute TLS/HTTP fingerprints for bot detection. compute_tls/compute_http build fingerprints from user-supplied lists; parse_client_hello parses a raw ClientHello record (hex) and emits JA3 + JA4 from the real wire bytes; analyze_request links a captured requestId.
This record as markdown: /tools/io-github-vmoranv-jshookmcp/network-tls-fingerprint.md
What network_tls_fingerprint does on Jshookmcp
AI agents invoke network_tls_fingerprint to trigger actions in Jshookmcp. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why network_tls_fingerprint is rated High
Executes fingerprint computation and network request analysis with user-supplied inputs.
From the tool's definition compute TLS/HTTP fingerprints, parse raw ClientHello, analyze captured request
Attacks that exploit this kind of access
The rule that runs network_tls_fingerprint safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Jshookmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For network_tls_fingerprint, this is the rule to start with:
network_tls_fingerprint stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Jshookmcp, apply this rule, and every network_tls_fingerprint call is checked against it from then on.
Questions about network_tls_fingerprint
Compute TLS/HTTP fingerprints for bot detection. compute_tls/compute_http build fingerprints from user-supplied lists; parse_client_hello parses a raw ClientHello record (hex) and emits JA3 + JA4 from the real wire bytes; analyze_request links a captured requestId. It is categorised as a Execute tool in the Jshookmcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Jshook MCP server in PolicyLayer and add a rule for network_tls_fingerprint: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Jshookmcp. Nothing to install.
network_tls_fingerprint is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the network_tls_fingerprint rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for network_tls_fingerprint. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
network_tls_fingerprint is provided by the Jshook MCP server (@jshookmcp/jshook). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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