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marc-shade

Threat Intelligence MCP Server

by marc-shade

check_network_against_threats

Analyze network scan results to identify potential security threats by comparing device IPs against multiple threat intelligence sources.

Instructions

Check network scan results against threat intelligence.

Args: scan_results: JSON string from network scanner with device IPs

Returns: JSON with any matched threats

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scan_resultsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool checks against threat intelligence and returns JSON with matches, but lacks critical details: whether this is a read-only operation, if it requires authentication, rate limits, what happens on errors, or if it modifies any state (e.g., updates a cache). For a security tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by structured 'Args' and 'Returns' sections. Each sentence earns its place by providing essential information without redundancy. Minor improvements could include integrating the sections more fluidly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (security analysis), no annotations, and an output schema exists (implied by 'Returns: JSON'), the description is moderately complete. It covers the basic operation and parameter semantics but lacks behavioral context (e.g., safety, performance) and usage guidelines. The output schema reduces the need to explain return values, but more context is needed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that 'scan_results' is a 'JSON string from network scanner with device IPs', which clarifies the parameter's format and content beyond the schema's generic 'string' type. However, it doesn't detail the exact JSON structure or provide examples, leaving some ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check network scan results against threat intelligence.' It specifies the verb ('check') and resource ('network scan results'), and distinguishes it from siblings like check_ip_reputation by focusing on bulk scan results rather than individual IPs. However, it doesn't explicitly differentiate from check_bulk_ips, which might be a similar sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like check_bulk_ips or check_ip_reputation. It mentions 'scan results' but doesn't clarify prerequisites (e.g., requires prior network scanning) or exclusions (e.g., not for single IPs). This leaves the agent to infer usage from context alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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