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1sarthakbhardwaj

Labellerr MCP Server

project_create

Create a new annotation project with dataset, data type, and annotation guidelines. Set up rotation options and upload files to prepare for labeling.

Instructions

Create a new annotation project with dataset and guidelines

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
autolabelNoEnable auto-labeling
data_typeYesType of data to annotate
created_byYesEmail of the creator
dataset_nameYesName of the dataset
project_nameYesName of the project
files_to_uploadNoArray of file paths to upload
rotation_configNo
annotation_guideYesArray of annotation questions/guidelines
folder_to_uploadNoPath to folder containing files to upload
dataset_descriptionNoDescription of the dataset
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing side effects and requirements. It merely says 'Create' without mentioning that this operation may involve file uploads, create associated resources, or require specific permissions. The agent has no warning about the operation's mutating impact or potential asynchronous behavior.

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 a single sentence with no superfluous text. It front-loads the action ('Create') and includes the essential object ('annotation project') and key components ('dataset and guidelines'). This is concise and well-structured, despite being minimal.

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 the tool's complexity (10 parameters, nested objects, no output schema), the description is far too sparse to be complete. It does not explain what happens after creation (e.g., return value), whether the dataset must pre-exist, or how options like autolabel and rotation_config affect the project. The agent would need to infer all of this from the schema alone.

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?

The input schema covers 90% of parameters with detailed descriptions, so the baseline for this dimension is 3. The tool description adds minimal semantic value by highlighting 'dataset and guidelines' as key components, but it does not explain how these relate to other parameters like files_to_upload or rotation_config. The schema already does the heavy lifting.

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 the action ('Create') and the resource ('a new annotation project'), and it specifies the core inputs ('with dataset and guidelines'). This distinguishes it from sibling tools like dataset_create, which focus on dataset creation rather than project creation.

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 such as dataset_create or project_update_rotation. It does not mention prerequisites (e.g., whether the dataset must already exist) or exclusion criteria, leaving the agent to infer usage from the schema alone.

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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