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stanfrbd

Cyberbro MCP Server

by stanfrbd

analyze_observable

Analyze IPs, domains, URLs, hashes, or Chrome extension IDs for security threats using multiple threat intelligence engines to assess reputation and identify potential indicators of compromise.

Instructions

Trigger an analysis for a given observable (IP, domain, URL, hash, chrome extension id) using Cyberbro. It can support multiple observables at once separated by spaces. Args: text: Observable(s) to analyze. engines: List of engine names. Returns: The analysis response from Cyberbro API.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
enginesYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'can support multiple observables at once' and returns 'The analysis response from Cyberbro API,' but fails to disclose critical behavioral traits such as whether this is a read-only or mutative operation, authentication requirements, rate limits, error handling, or what the response format entails. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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, with the core purpose stated in the first sentence. The additional sentences about multiple observables and return values are relevant, but the 'Args:' and 'Returns:' sections could be integrated more seamlessly. Overall, it avoids unnecessary verbosity, though minor structural improvements could enhance clarity.

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

Completeness2/5

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

Given the complexity of an analysis tool with no annotations, no output schema, and low schema description coverage (0%), the description is incomplete. It lacks details on behavioral aspects (e.g., safety, permissions), parameter constraints (e.g., valid engine names), and output specifics (e.g., response structure). For a tool that likely involves API calls and potentially sensitive data, this leaves too many unknowns for effective agent 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?

Schema description coverage is 0%, so the description must compensate. It adds some meaning by explaining that 'text' accepts 'Observable(s) to analyze' and can include 'multiple observables at once separated by spaces,' and that 'engines' is a 'List of engine names.' However, it does not specify what constitutes valid observables (e.g., format examples) or available engine names, leaving parameters partially undocumented. This provides marginal value over the bare schema, aligning with the baseline for incomplete coverage.

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: 'Trigger an analysis for a given observable... using Cyberbro.' It specifies the verb ('trigger an analysis') and resource ('observable'), and lists the supported observable types (IP, domain, URL, hash, chrome extension id). However, without sibling tools, it cannot demonstrate differentiation from alternatives, preventing a perfect score.

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

Usage Guidelines3/5

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

The description implies usage by mentioning support for 'multiple observables at once separated by spaces,' which suggests a context for batch processing. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., for single vs. multiple observables, specific engine choices), and there are no sibling tools to compare against, so the guidance remains implicit rather than explicit.

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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