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markuskreitzer

PicoScope MCP Server

start_streaming

Initiates real-time data acquisition from PicoScope oscilloscopes with configurable sampling intervals, buffer sizes, and capture duration for continuous or limited signal streaming.

Instructions

Start streaming data acquisition.

Args: sample_interval_ns: Sample interval in nanoseconds. buffer_size: Size of streaming buffer. auto_stop: Whether to automatically stop after max_samples. max_samples: Maximum samples to capture (0 = continuous).

Returns: Dictionary containing streaming status and configuration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sample_interval_nsYes
buffer_sizeNo
auto_stopNo
max_samplesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The MCP tool handler for 'start_streaming'. Decorated with @mcp.tool(), defines input schema via type hints, and contains the core logic (currently stubbed with TODO).
    @mcp.tool()
    def start_streaming(
        sample_interval_ns: int,
        buffer_size: int = 100000,
        auto_stop: bool = False,
        max_samples: int = 0,
    ) -> dict[str, Any]:
        """Start streaming data acquisition.
    
        Args:
            sample_interval_ns: Sample interval in nanoseconds.
            buffer_size: Size of streaming buffer.
            auto_stop: Whether to automatically stop after max_samples.
            max_samples: Maximum samples to capture (0 = continuous).
    
        Returns:
            Dictionary containing streaming status and configuration.
        """
        # TODO: Implement streaming mode
        return {
            "status": "not_implemented",
            "sample_interval_ns": sample_interval_ns,
            "buffer_size": buffer_size,
        }
  • Top-level registration call in the main server file that invokes register_acquisition_tools(mcp), thereby registering the 'start_streaming' tool among others.
    register_acquisition_tools(mcp)
  • Supporting method in the device manager class intended for the actual streaming implementation, called potentially from the tool handler.
    def start_streaming(
        self, sample_interval_ns: int, buffer_size: int
    ) -> bool:
        """Start streaming mode.
    
        Args:
            sample_interval_ns: Sample interval in nanoseconds.
            buffer_size: Buffer size for streaming.
    
        Returns:
            True if successful, False otherwise.
        """
        if not self.is_connected():
            return False
    
        # TODO: Implement streaming mode
        # This will need to set up buffers and call ps*RunStreaming
    
        return True
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions that the tool 'starts streaming' and returns a status dictionary, but doesn't cover critical aspects like whether this requires a connected device, if it's destructive to existing data, error conditions, or rate limits. The description is insufficient for a mutation tool with zero annotation coverage.

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

Conciseness5/5

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

The description is efficiently structured with a clear purpose statement followed by well-organized parameter explanations in bullet-like format. Every sentence adds value, with no redundant information. The 'Args' and 'Returns' sections are appropriately front-loaded and concise.

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 (starting a streaming process with 4 parameters) and the presence of an output schema (which handles return value documentation), the description is moderately complete. It explains parameters well but lacks crucial context about prerequisites, behavioral implications, and differentiation from sibling tools, making it inadequate for confident usage.

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

Parameters4/5

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

The description provides meaningful explanations for all 4 parameters beyond the 0% schema description coverage. It clarifies that 'sample_interval_ns' is in nanoseconds, 'buffer_size' is for streaming, 'auto_stop' controls automatic stopping, and 'max_samples=0' means continuous capture. This compensates well for the lack of schema descriptions.

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 with a specific verb ('Start streaming data acquisition'), which distinguishes it from sibling tools like 'stop_streaming' and 'get_streaming_data'. However, it doesn't explicitly differentiate from other data acquisition methods like 'capture_block', leaving some ambiguity about when to choose streaming versus block capture.

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 'capture_block' for data acquisition. It mentions parameters like 'auto_stop' and 'max_samples' but doesn't explain the trade-offs between continuous streaming and finite capture, or prerequisites like device connection.

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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