Agentmetry vs CuratedMCP: Which is Better in 2026?
A comprehensive comparison of Agentmetry and CuratedMCP covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Agentmetry if:
- →You want more affordable paid plans (from $2/mo)
- →You need records agent activity at the tool boundary or mitre att&ck technique tagging per event
Choose CuratedMCP if:
- →You need one-command discovery of mcp servers across six ai clients or a–f machine grade against a risk-classified catalog of 70+ reviewed servers
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Agentmetry vs CuratedMCP: At a Glance
Pricing Comparison: Agentmetry vs CuratedMCP
Understanding the pricing differences between Agentmetry and CuratedMCP is crucial for making the right choice. Here's how their plans compare side by side.
Agentmetry Pricing
CuratedMCP Pricing
💡 Pricing takeaway: Both Agentmetry and CuratedMCP offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Agentmetry and CuratedMCP stacks up.
What Makes Each Tool Unique
🔵 Unique to Agentmetry
Features available in Agentmetry but not in CuratedMCP:
- ✓Records agent activity at the tool boundary
- ✓MITRE ATT&CK technique tagging per event
- ✓Sequence correlation into single critical alerts
- ✓Runs fully local with zero cloud calls
- ✓Secret values excluded from the trail
- ✓SIEM-readable output format
🟣 Unique to CuratedMCP
Features available in CuratedMCP but not in Agentmetry:
- ✓One-command discovery of MCP servers across six AI clients
- ✓A–F machine grade against a risk-classified catalog of 70+ reviewed servers
- ✓Runs locally; no config data leaves the machine, no account needed
- ✓Non-zero exit on high-risk findings for use as a CI gate
- ✓Catalog queryable from an AI client via CuratedMCP's own MCP server
- ✓Hub installs a server once and syncs it to every client
Use Case Recommendations
Best for: Agentmetry
Agentmetry is a local flight recorder for AI coding agents, written for the security engineer who found out their company was running Cursor by reading a pull request. Endpoint detection sees a process; it does not see that an agent read a private SSH key and then made an outbound network call in the same session. Agentmetry records agent activity at the tool boundary — every read, shell command and fetch — correlates the sequence, tags it against MITRE ATT&CK technique IDs, and raises a single critical alert for the pattern rather than a stream of individually unremarkable events. The worked example on the homepage is exactly that: a `cat ~/.ssh/id_rsa` read tagged T1552.004, an `aws configure list` shell call tagged T1059 with DLP flagging an AWS access key, and a WebFetch to a paste site tagged T1071.001, correlated into one credential-exfil finding. Secret values themselves are never written to the trail. It runs entirely on the machine with zero cloud calls, ships nine detection rules in the current phase, and emits in a format existing SIEMs already read, so it slots into an established pipeline rather than becoming another console. Install is a git clone plus `pip install -e`, with a PowerShell installer for Windows. The whole thing is Apache-2.0 open source with the code public, which matters for a tool whose entire value proposition is that it watches privileged activity.
Ideal use cases:
- •Teams or individuals who need records agent activity at the tool boundary
- •Teams or individuals who need mitre att&ck technique tagging per event
- •Teams or individuals who need sequence correlation into single critical alerts
- •Teams or individuals who need runs fully local with zero cloud calls
- •Anyone focused on security workflows
- •Anyone focused on agents workflows
Best for: CuratedMCP
CuratedMCP addresses a blind spot that appeared as soon as MCP took hold: servers are configured per-machine and per-client, which makes them invisible to any network-side AI gateway. CuratedMCP puts an open-source agent on the endpoint instead. One command discovers every MCP server configured across Claude Code, Claude Desktop, Cursor, Windsurf, GitHub Copilot and Gemini, flags the shadow servers nobody approved, and grades the machine A–F against a risk-classified catalog of over 70 human-reviewed servers — in roughly 60 seconds, entirely locally, with no configuration data leaving the machine and no account required. The scan exits non-zero on high-risk findings, which makes it usable directly as a CI gate rather than only as a report. The same catalog is queryable from an AI client via CuratedMCP's own MCP server, so an agent can check a server's risk classification before installing it. The Hub component installs a server once and syncs it across every client, replacing the per-client config sprawl that causes shadow servers in the first place. Two further components are in earlier stages: Sentinel, a local policy guard in early access, and TokenShield, a measured token ledger in beta. The product is MIT licensed and free for individual developers; the commercial tier is fleet-wide visibility, an org allowlist pushed to every machine, and an audit log of MCP tool calls for engineering organisations.
Ideal use cases:
- •Teams or individuals who need one-command discovery of mcp servers across six ai clients
- •Teams or individuals who need a–f machine grade against a risk-classified catalog of 70+ reviewed servers
- •Teams or individuals who need runs locally; no config data leaves the machine, no account needed
- •Teams or individuals who need non-zero exit on high-risk findings for use as a ci gate
- •Anyone focused on mcp workflows
- •Anyone focused on security workflows
🛡️ Other AI Security & Testing Tools to Consider
Agentmetry and CuratedMCP aren't the only options. Here are other popular tools in the same space:
Lineation
Security control plane for AI agents — zero-trust agent identity, LLM and MCP gateways, policy-as-code, and prompt-injection defense
Axtary
Payload-bound authorization for AI agents — human approval is cryptographically tied to the exact action, so a changed payload is denied
Tracecat
Open-source SOAR for AI-native security teams — agents, cases, and workflows with human approval gates
Trestle
Local secret scanner with an MCP server so coding agents check their own output
ZeroLeaks
Continuous AI red teaming for agents, endpoints and MCP tools, with unlimited scans on every plan
Bot Butcher
LLM-based spam classification API for contact forms — a reCAPTCHA alternative with no visitor friction
Is one of these your tool?
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Frequently Asked Questions
Is Agentmetry better than CuratedMCP?
It depends on your needs. Agentmetry offers 6 key features including Records agent activity at the tool boundary and MITRE ATT&CK technique tagging per event, while CuratedMCP provides 6 features including One-command discovery of MCP servers across six AI clients and A–F machine grade against a risk-classified catalog of 70+ reviewed servers. Agentmetry uses a open-source model with a free tier, while CuratedMCP is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Agentmetry cheaper than CuratedMCP?
Agentmetry is cheaper, starting at Apache-2.0 open source, installed from GitHub. No paid tier or pricing page is published as of August 2026. compared to CuratedMCP's $29/year. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Agentmetry and CuratedMCP together?
Yes, many users combine Agentmetry and CuratedMCP in their workflow. Agentmetry excels at records agent activity at the tool boundary, while CuratedMCP shines with one-command discovery of mcp servers across six ai clients. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Agentmetry and CuratedMCP?
While both are ai security & testing tools, Agentmetry emphasizes records agent activity at the tool boundary, whereas CuratedMCP is known for one-command discovery of mcp servers across six ai clients. The best choice depends on your specific workflow and feature priorities.
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