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List Projects

list_projects

Retrieve a paginated list of projects, filtered by programming language, to identify project structure and scoping details for agent tasks.

Instructions

List all projects with optional filtering.

Retrieves a paginated list of projects, optionally filtered by programming language.

Project Scoping: Each project may include project_structure metadata that defines:

  • src_dirs: Recommended source directories (e.g., ["src/", "lib/"])

  • test_dirs: Test directories (e.g., ["tests/"])

  • entry_points: Main entry points (e.g., ["src/main.py"])

  • exclude: Paths to ignore (e.g., [".venv/", "pycache/"])

Use get_project to retrieve full structure details for a specific project, then use this information when scoping agent work via refine_prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYesList projects request with optional filters

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitYes
offsetYes
projectsYes
total_countYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

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 full burden of behavioral disclosure. It does disclose pagination, optional filtering, and the variable project_structure metadata, which is useful, but it omits ordering, error behavior, authentication needs, and an explicit read-only statement.

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 a clear summary and uses bullet points effectively for project_structure metadata. The first two sentences are somewhat redundant, which prevents a perfect score, but the overall structure is well organized.

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 paginated list tool with a rich output schema and well-documented parameters, the description covers pagination, filtering, and downstream use of project_structure metadata. It does not address ordering or empty-result behavior, but these are not critical for successful invocation.

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?

Schema description coverage is 100%, so the baseline is 3. The description confirms that main_language is the filter and implies pagination via limit/offset, but it adds no additional semantic meaning beyond what the schema already provides.

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 uses a specific verb and resource: 'List all projects' and 'Retrieves a paginated list of projects'. It distinguishes itself from get_project by pointing out that full structure details belong to get_project, clarifying the division of labor between siblings.

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 explicitly directs agents to use get_project when full structure details for a specific project are needed, and mentions refine_prompt for scoping work. It does not state explicit 'when not to use' conditions, but the routing context is clear enough.

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