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save_partial_finding

Saves partial penetration testing findings like open ports and protocols for later analysis when data is too long or already exists elsewhere.

Instructions

save partial findings for later use (like open ports, used protocols, versions etc.) if finding data is too long or already exists in another file dont save it

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYes
contentYes

Implementation Reference

  • The handler function decorated with @mcp.tool for the 'save_partial_finding' tool. It creates the project directory if it doesn't exist and uses the save_file helper to write the provided content to the specified filename in the project directory.
    @mcp.tool(name="save_partial_finding",description="save partial findings for later use (like open ports, used protocols, versions etc.) if finding data is too long or already exists in another file dont save it")
    def save_partial_finding(filename: str,content: str):
    
        
    
        os.makedirs(config.PROJECT_DIRECTORY, exist_ok=True)
    
    
        save_file(config.PROJECT_DIRECTORY, filename, content)
  • Helper function used by save_partial_finding to save content to a file in the specified directory, with validation and error handling.
    def save_file(directory: str, filename: str, content: str) -> bool:
      
        try:
            if not directory or not filename:
                raise ValueError("Directory and filename cannot be empty")
                
            if not isinstance(content, str):
                raise ValueError("Content must be a string")
            
            dir_path = Path(directory)
            
            dir_path.mkdir(parents=True, exist_ok=True)
            
            file_path = dir_path / filename
            
            with open(file_path, 'w', encoding='utf-8') as file:
                file.write(content)
                
            print(f"File saved successfully: {file_path}")
            return True
            
        except Exception as e:
            print(f"Error saving file: {str(e)}")
            return False
  • The @mcp.tool decorator registers the save_partial_finding function as an MCP tool.
    @mcp.tool(name="save_partial_finding",description="save partial findings for later use (like open ports, used protocols, versions etc.) if finding data is too long or already exists in another file dont save it")
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. It discloses behavioral traits such as conditional saving based on data length or duplication, but lacks details on permissions, error handling, or response format. For a tool with no annotations, this is insufficient as it doesn't cover critical aspects like what happens on success/failure or storage implications.

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

Conciseness3/5

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

The description is a single run-on sentence that could be better structured. It front-loads the purpose but includes conditional logic ('if...dont save it') that makes it slightly verbose. While not wasteful, it lacks clarity in phrasing and could be more concise by separating concepts.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It covers the basic purpose and usage conditions but misses parameter details, behavioral context, and output information. For a tool with two parameters and conditional logic, this leaves significant gaps in understanding.

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 does not add any meaning beyond the input schema, which only lists 'filename' and 'content' with no details. The description mentions 'finding data' but doesn't explain parameter semantics, formats, or constraints. This leaves parameters largely undocumented.

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: 'save partial findings for later use' with examples like 'open ports, used protocols, versions etc.' It specifies the verb ('save') and resource ('partial findings'), but does not explicitly differentiate from sibling tools, which include other data processing tools like 'read_files' or 'upload_zip_to_bloodhound'. The purpose is specific but lacks sibling distinction.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: 'if finding data is too long or already exists in another file dont save it'. This gives explicit conditions for usage (data length or duplication) but does not mention alternatives or when not to use it relative to sibling tools. The guidance is helpful but not comprehensive.

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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