AgentSec MCP
Server Details
Security intelligence via x402 on Base. CVE lookup, IP reputation, secret scanning.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.8/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: CVE lookup for software vulnerabilities, reputation check for IPs/domains, and secret scanning for credentials. No functional overlap exists.
All tools follow a consistent noun_verb pattern (cve_lookup, reputation_check, secret_scan), making the naming predictable and easy to understand.
With 3 tools, the server is well-scoped for a security analysis assistant, covering vulnerability lookup, IP/domain reputation, and secret detection without unnecessary bloat.
The tools cover three essential security areas, but common additions like malware hash lookup or threat intelligence feeds are missing, representing minor gaps in a broader security toolkit.
Available Tools
3 toolscve_lookupAInspect
Look up known CVE vulnerabilities for an npm package+version or a specific CVE ID. Returns CVSS scores, descriptions, and fix versions from NVD and OSV. Results are cached for 10 minutes. Costs $0.01 USDC per call.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses caching (10 minutes) and cost ($0.01 per call), which adds value beyond the basic purpose. It does not cover rate limits or auth, but for a simple read-only lookup, this is sufficient transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: first sentence states purpose, second adds detail, third adds behavioral notes. No wasted words, and critical info is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers return values and behavioral aspects, but fails to explain how to provide the required input (package/version or CVE ID), given that the schema has no parameters. This omission makes the tool definition incomplete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, but the description implies the tool accepts a package+version or CVE ID. This is a significant discrepancy; the description adds misleading information instead of clarifying how to provide input. With 0 parameters, baseline is 4, but the mismatch makes the description harmful, not helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('look up') and the resource ('CVE vulnerabilities'), specifying the scope as either a npm package+version or a CVE ID. It effectively distinguishes from siblings like 'reputation_check' and 'secret_scan', which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for vulnerability lookups by stating what it returns, but it does not explicitly provide when-to-use or when-not-to-use guidance, nor does it mention alternatives. Sibling tools are different, so the lack of exclusions is acceptable but not a strength.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reputation_checkAInspect
Check the reputation of an IP address or domain using AbuseIPDB and VirusTotal. Returns a security verdict (malicious/suspicious/unknown/clean) with confidence signals. Verdict is conservative: incomplete data returns 'unknown' never 'clean'. Results are cached for 10 minutes. Costs $0.01 USDC per call.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses conservative verdict policy (unknown never clean), caching duration (10 minutes), and cost ($0.01 per call), providing rich behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: purpose, verdict details, and behavior/cost. Front-loaded with actionable information, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While behavioral details are provided, the description lacks an input parameter specification (IP/domain), making the tool's usage ambiguous. No output schema exists, and the description does not clarify the structure of confidence signals.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has zero parameters, but description implies the tool accepts an IP or domain as input. This contradiction makes the description misleading. Schema coverage is vacuously 100%, but description adds incorrect information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks reputation of an IP or domain using specific sources (AbuseIPDB, VirusTotal) and returns a security verdict. It is distinct from sibling tools cve_lookup and secret_scan.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance. However, the context of security reputation checking is clear from the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
secret_scanAInspect
Scan text content for hardcoded secrets, API keys, and credentials using 20 pre-compiled patterns.
Privacy guarantee: Input text is NEVER logged, cached, stored, or forwarded. Only findings_count and finding offsets (not matched values) are returned.
Detected pattern types include: AWS keys, GitHub/GitLab PATs, OpenAI/Anthropic keys, Stripe secrets, Slack tokens, PEM private keys, JWT tokens, and 13 more.
Per-call rate limit: 100/min. Payment: $0.05 USDC per scan.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It clearly discloses privacy guarantees (no logging/caching/storage/forwarding), return behavior (only counts and offsets), pattern types, rate limits, and cost, which is highly transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with four sentences covering purpose, privacy, return details, pattern list, and cost/limits, front-loaded with the primary function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite covering many aspects, the description fails to specify how the text content is provided to the tool since the input schema is empty. This is a significant gap for a tool that scans input text, making it incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the description does not need to explain individual parameters. With no parameters, the baseline is 4, and the description adds no param-specific info, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans text for hardcoded secrets using 20 patterns, which is a specific verb+resource. It is distinct from sibling tools cve_lookup and reputation_check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions. It implies usage for secret scanning but lacks guidance on comparative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseAqualityAmaintenanceSecurity intelligence API for AI models. CVE lookup with EPSS/KEV, domain recon (DNS, WHOIS, SSL, subdomains, WAF), and code security checks (secrets, injection, headers). 16 tools, no API key required.5532MIT
- Flicense-qualityDmaintenanceProvides threat intelligence and vulnerability research tools by integrating with NVD, VirusTotal, AbuseIPDB, Shodan, and MITRE ATT\&CK. It enables users to perform CVE lookups, analyze IP reputation, and retrieve detailed MITRE ATT\&CK technique information.1
- FlicenseAqualityDmaintenanceEnables searching and analyzing CVEs and vulnerabilities from multiple sources, optimized for PR review scenarios to help developers identify the latest security issues.82
- Alicense-qualityFmaintenanceProvides CVE search enriched with EPSS exploit likelihood and CISA KEV status, plus live IP/domain reputation and a real-time threat feed for AI agents.MIT