MCP Boundary

MCP Boundary

Blocks MCP tool calls with reasons the agent can act on

david7302
@david7302
Published on Jul 6, 2026
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Pricing
Freemium
Platforms
MCPDesktop

Discovery signals

How AI and people discover MCP Boundary on PeerPush

Read by AI engines 30dEngines readingHow many times AI crawlers read this listing, and which engines those crawlers belong to.
203
  • ChatGPT
  • Perplexity
  • Claude
  • +5 other AI crawlers

Reads by these AI engines

Crawlers behind these engines read this listing on PeerPush.
PersistenceRead streakConsecutive days, counting back from today, that AI has read this listing every single day.
28 days

Read by AI every day

Read by AI every day for the last 28 days.
MCP citations all-timeMCP citationsHow often AI tools pulled this listing through MCP, and its typical position in those results.
6 pulls

Top 4 in MCP results

AI tools pulled this listing via MCP, typically in the top 4 results.

About MCP Boundary

MCP Boundary is a local checkpoint between your AI agent - for example Claude Desktop - and the MCP tools it uses. You write the rules; Boundary checks each routed call against them before it runs. Nothing here is automatic - it does what your policy says. The point is not just to say no. When Boundary blocks a call, it sends back a short, machine-readable reason the agent can use, so the agent can adjust the request instead of failing or retrying blindly. Two things make that useful. State-aware writes. One rule you can set is to tie a write to the data the agent read first. Then, if that data changed in the meantime, Boundary stops the write and tells the agent to read again before acting. A valid call is not always a safe outcome. Clear feedback, and human approval when it matters. A block is not a dead end. The reason can be specific - for example, which email recipients were rejected, so the agent can retry without them. And for a call that needs a person's OK, nothing runs: the agent gets a clear "waiting for approval" answer and pauses, while a human approves or rejects the exact action in the local dashboard. Only an approved follow-up call goes through. Your rules can reach into the details of a call, not just its name: which recipients are allowed, which values are permitted, how much data can come back, how often a call may repeat. Every checked call - the decision, the reason, the result - lands in a local activity view, so you can see what the agent tried and why it was allowed or blocked. It all runs on your machine. The agent sees the tools, never the passwords or tokens behind them, and nothing is sent back to us. About prompt injection: Boundary does not try to detect a jailbreak or a poisoned instruction. It works one layer lower. Even if the model is tricked, routed calls can still only do what your policy allows - so a risky send, write, or delete that your policy blocks stays blocked, or waits for a human, no matter why the agent asked. Honest scope: Boundary only checks calls that actually run through it. It does not vouch for what an MCP server does inside, and it is a local tool for developers and operators — not a hosted security gateway or a data-loss filter.

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Comments (2)

wafler
@wafler

We can see this becoming a go-to in the space. And if you ever need DDoS protection with a proper firewall for your website, just let us know!

david7302
@david7302

MCP Boundary is an early local tool for checking routed MCP calls before they reach the server. It blocks or asks for approval, and gives agents usable reasons instead of just "denied".