Skip to main content
Glama
rspace-os

RSpace MCP Server

Official
by rspace-os

duplicate_sample

Create exact copies of existing samples for parallel experiments or backup purposes in RSpace research data management.

Instructions

Creates an exact copy of an existing sample

Usage: Replicate samples for parallel experiments or backup Returns: New sample information with fresh ID and subsamples

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sample_idYes
new_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:803-812 (handler)
    The main handler function for the 'duplicate_sample' tool. It is registered via the @mcp.tool decorator and implements the tool logic by calling the RSpace inventory client's duplicate method.
    @mcp.tool(tags={"rspace", "inventory", "samples"})
    def duplicate_sample(sample_id: Union[int, str], new_name: str = None) -> dict:
        """
        Creates an exact copy of an existing sample
        
        Usage: Replicate samples for parallel experiments or backup
        Returns: New sample information with fresh ID and subsamples
        """
        return inv_cli.duplicate(sample_id, new_name)
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool creates an exact copy and returns new sample information with fresh ID and subsamples, which provides some behavioral context about what gets created. However, it doesn't disclose important behavioral traits like whether this requires specific permissions, if there are rate limits, what happens if the source sample doesn't exist, or whether the duplication is immediate or queued.

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 perfectly concise with three sentences that each earn their place: the core functionality, usage context, and return value. It's front-loaded with the main purpose and wastes no words. The structure moves logically from what it does to when to use it to what it returns.

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 there's an output schema (which handles return values), no annotations, and 2 parameters with 0% schema coverage, the description provides adequate but incomplete context. It covers the purpose and usage well but lacks parameter explanations and behavioral details like error conditions or permissions needed. For a mutation tool (creates new resources) with no annotations, it should provide more safety/behavioral context.

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?

The schema description coverage is 0%, meaning neither parameter has documentation in the schema. The description mentions 'sample' and 'new sample information' but doesn't explain what 'sample_id' should be (integer or string format) or what 'new_name' does (whether it renames the duplicate or is optional). With 2 undocumented parameters, the description adds minimal semantic value beyond what's implied by the tool name.

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 creates an exact copy of an existing sample, which is a specific verb (creates/duplicate) and resource (sample). It distinguishes from siblings like 'create_sample' by specifying it duplicates existing samples rather than creating new ones from scratch. However, it doesn't explicitly differentiate from 'bulk_create_samples' or other sample-related tools.

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: 'Replicate samples for parallel experiments or backup.' This gives practical guidance on appropriate use cases. However, it doesn't specify when NOT to use it or mention alternatives like 'create_sample' for creating new samples from scratch rather than duplicating existing ones.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rspace-os/rspace-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server