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Cyberbro MCP Server

by stanfrbd

get_analysis_results

Retrieve analysis results from Cyberbro API using a specific analysis ID to get threat intelligence about Indicators of Compromise.

Instructions

Retrieve the results of a previous analysis by analysis_id. Args: analysis_id: Analysis ID to retrieve results for. Returns: The analysis results from Cyberbro API.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
analysis_idYes

Implementation Reference

  • The `get_analysis_results` tool handler. It is an async function registered via `@mcp.tool()` decorator. It takes an `analysis_id` string parameter, makes a GET request to `{config.cyberbro_api}/results/{analysis_id}` via `get_json()`, and returns the analysis results from the Cyberbro API. Errors are caught and returned as an error dict.
    @mcp.tool()
    async def get_analysis_results(analysis_id: str) -> Any:
        """
        Retrieve the results of a previous analysis by analysis_id.
        Args:
            analysis_id: Analysis ID to retrieve results for.
        Returns:
            The analysis results from Cyberbro API.
        """
        try:
            return await get_json(
                f"{config.cyberbro_api}/results/{analysis_id}", verify=config.ssl_verify
            )
        except Exception as exc:
            return {"error": f"Error executing tool get_analysis_results: {exc!s}"}
  • Registration of all analysis tools (including get_analysis_results) via `register_analysis_tools(mcp, config)` called from `create_server()`. The tool is registered when the FastMCP server is created.
    register_analysis_tools(mcp, config)
  • The `get_json` helper function used by `get_analysis_results` to perform the HTTP GET request to the Cyberbro API endpoint.
    async def get_json(url: str, verify: bool):
        async with httpx.AsyncClient(verify=verify) as client:
            response = await client.get(url)
            response.raise_for_status()
            return response.json()
  • The `cyberbro_api` property on `CyberbroConfig` which constructs the base API URL (`{cyberbro_url}/{api_prefix}`) used to build the full results endpoint URL.
    @property
    def cyberbro_api(self) -> str:
        return f"{self.cyberbro_url}/{self.api_prefix}"
  • Test that verifies the `get_analysis_results` tool is registered and functional, calling it with `'42'` and asserting the result.
    results_result = await mcp.registered_tools["get_analysis_results"]("42")
Behavior2/5

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

No annotations are provided, so the description carries full disclosure burden. It describes a read operation ('retrieve') but does not disclose error behavior (e.g., if analysis_id is invalid), idempotency, or any side effects. The description adds minimal value beyond the tool name and schema.

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 short and uses a clear docstring format with Args and Returns sections. Every sentence serves a purpose, but the Returns section is vague ('analysis results from Cyberbro API'). Still, it is front-loaded and efficient.

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?

For a tool with one parameter and no output schema, the description should cover the return format, error cases, and lifecycle expectations. It only states 'the analysis results' generically. Combined with no annotations, the agent lacks context on when results are available or how to interpret them.

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

Parameters2/5

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 restates the parameter as 'analysis_id: Analysis ID to retrieve results for', which adds little meaning beyond the schema's 'title'. No constraints, formats, or examples are provided, leaving the parameter under-documented.

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 'Retrieve the results of a previous analysis by analysis_id', specifying the verb, resource, and key parameter. This distinguishes it from siblings like analyze_observable (starts analysis) and is_analysis_complete (checks status), making the purpose unambiguous.

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 after an analysis is started, but does not explicitly state when to use it versus alternatives such as is_analysis_complete or get_web_url. No conditions, prerequisites, or when-not-to-use information is provided.

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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