list_projects

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

No annotations are provided, so the description carries the burden. It discloses the return type (JSON object) and data source (databases.json), listing fields like 'default_project' and 'projects'. It does not mention authorization or side effects, but for a read-only listing tool, this level of transparency is adequate.

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

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

The description is concise, with three short segments: purpose, usage advice, and return format. It front-loads the key action and resource, and every sentence adds value without redundancy.

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

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

Given zero parameters and an output schema described inline, the description is complete. It fully explains the tool's behavior, when to call it, and the structure of results, leaving no ambiguity for an AI agent.

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

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

The tool has no parameters, and schema description coverage is 100% (empty schema). The description adds context about the returned structure, which enhances understanding beyond the schema, earning a baseline score of 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all configured database projects and their access modes, using specific verb 'list' and resource 'database projects'. It distinguishes itself from sibling tools like 'execute_postgres' or 'describe_postgres_schema' which target different operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises 'Call this first when you don't know which project_id to use', providing a clear usage scenario. However, it does not mention when not to use it or suggest alternatives, but given the context, this guidance is sufficient.

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