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stanfrbd

Cyberbro MCP Server

by stanfrbd

analyze_observable

Submit observables (IP, domain, URL, hash, extension ID) for threat analysis using specified engines. Returns enriched reputation data from Cyberbro API.

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

Implementation Reference

  • The async function analyze_observable is the handler for the tool. It takes text and engines parameters, POSTs to Cyberbro's /analyze endpoint, and returns the API response (or an error dict on exception).
    @mcp.tool()
    async def analyze_observable(text: str, engines: list[str]) -> Any:
        """
        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.
        """
        try:
            payload = {"text": text, "engines": engines}
            return await post_json(
                f"{config.cyberbro_api}/analyze", payload=payload, verify=config.ssl_verify
            )
        except Exception as exc:
            return {"error": f"Error executing tool analyze_observable: {exc!s}"}
  • The function register_analysis_tools wraps the @mcp.tool() decorator registration for analyze_observable (and sibling tools).
    def register_analysis_tools(mcp: FastMCP, config: CyberbroConfig) -> None:
  • The post_json helper function used by analyze_observable to send the HTTP POST request to the Cyberbro API.
    async def post_json(url: str, payload: dict, verify: bool):
        async with httpx.AsyncClient(verify=verify) as client:
            response = await client.post(url, json=payload)
            response.raise_for_status()
            return response.json()
  • CyberbroConfig.cyberbro_api property builds the base API URL used as the endpoint for the analyze_observable tool.
    @property
    def cyberbro_api(self) -> str:
        return f"{self.cyberbro_url}/{self.api_prefix}"
Behavior2/5

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

No annotations provided, so description must fully disclose behavior. It lacks details on error handling, rate limits, response time, or side effects. Simply states it triggers analysis and returns response.

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?

Short and direct, with front-loaded purpose. Uses Args/Returns structure but still efficient. No redundant sentences.

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?

Missing key context: no output schema, no explanation of asynchronous behavior, no details on response format. With siblings like get_analysis_results, description should clarify that this returns initial response, not final results.

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?

Adds meaning to parameters by explaining 'text' as observable(s) and 'engines' as list of engine names. Notes multiple observables separated by spaces, but does not specify engine source or format constraints beyond schema.

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

Purpose5/5

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

The description clearly states it triggers analysis for observables (IP, domain, URL, hash, chrome extension id) using Cyberbro. It distinguishes from siblings like get_analysis_results and get_engines by indicating it initiates analysis.

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?

Implies usage for initiating analysis, but no explicit when-to-use or alternatives compared to siblings. Mentions support for multiple observables, offering some guidance.

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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