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Create Project

create_project

Create a project to organize prompts by defining language, dependencies, and structure, enabling context-aware prompt selection for downstream agents.

Instructions

Create a new project for organizing prompts.

Creates a project with language and dependency metadata to enable better prompt selection based on project context.

Project Structure & Scoping: Projects can define their default structure via project_structure:

Default directories agents should focus on (e.g., ["src/", "tests/"])
Default exclusions (e.g., [".venv/", "node_modules/"])
Known entry points (e.g., ["src/main.py", "src/app.py"])

This helps downstream agents understand the project layout and scope their work appropriately without needing to explore the entire codebase.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes	Project creation request

Output Schema

TableJSON Schema

Name	Required	Description	Default
`success`	No
`created_at`	Yes
`project_id`	Yes
`description`	No
`project_name`	Yes
`project_structure`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 20, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It usefully discloses how project_structure is interpreted (focus directories, exclusions, entry points) and its effect on downstream agents. However, it does not disclose idempotency, duplicate handling, validation, or any error/side-effect behavior beyond the obvious creation.

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 front-loaded with the core purpose and then uses a scoped section for project_structure details. It is longer than strictly necessary, but the extra detail earns its place by clarifying a non-obvious parameter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a creation tool with a nested request schema and an output schema, the description covers the main purpose and the most complex parameter's behavior. It does not discuss duplicate-name behavior or edge cases, but the structured schema and output schema cover most remaining invocation details.

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?

Schema coverage is 100%, so the baseline is met. The description adds meaningful semantics by explaining that project_structure defines 'default directories agents should focus on', 'default exclusions', and 'known entry points', which goes beyond the schema's terse 'Project structure hints'.

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 states a specific action ('Create a new project') and resource ('project for organizing prompts'), and adds the metadata purpose ('language and dependency metadata'). It is clearly distinct from the sibling get/list/update tools, so an agent can infer what this tool is for.

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?

Usage is implied rather than explicit: the description says 'Create a new project' and explains downstream benefits, but it never states when to prefer this over get_project or list_projects, nor does it mention preconditions such as checking for an existing project or naming constraints.

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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