prepare_legacy_project_for_ai

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 mentions 'orchestrating' steps but doesn't explain what this entails operationally—e.g., whether it modifies files, requires specific permissions, has side effects like overwriting data, or handles errors. Terms like 'prepare' are ambiguous, failing to clarify if this is a read-only or mutative operation, which is critical for a tool with 11 parameters.

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 a single, efficient sentence that front-loads the main purpose. It avoids unnecessary words, but it could be more structured by explicitly listing key actions or outcomes. Given the complexity of the tool, a bit more detail might be warranted, but it's appropriately concise for its length.

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 the tool's high complexity (11 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is incomplete. It doesn't explain what 'preparing' involves, how parameters interact, or what the orchestration results in, leaving significant gaps for the agent to infer behavior. The output schema helps with return values, but the description fails to provide necessary context for safe and effective use.

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?

Schema description coverage is 0%, so the description must compensate by explaining parameters. It mentions 'ensure, intake, baseline, and context index steps,' which loosely relate to some parameters (e.g., 'runEnsureProjectMcp', 'refreshBaseline'), but doesn't define what these steps do or how parameters like 'sourceDir', 'autoApply', or 'maxDiffLines' affect them. This leaves most of the 11 parameters undocumented and their purposes unclear.

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 states the tool 'prepare[s] a legacy/existing project for high-quality AI delivery' and mentions it orchestrates 'ensure, intake, baseline, and context index steps,' which gives a general sense of its function. However, it's vague about what 'preparing' entails specifically (e.g., what changes are made) and doesn't clearly differentiate from sibling tools like 'ensure_project_mcp_current' or 'build_ai_context_index,' which might handle similar steps individually.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing, or exclusions, such as whether it's for new vs. existing projects or how it relates to sibling tools like 'analyze_legacy_project_baseline' or 'collect_legacy_project_intake.' This leaves the agent without clear direction on appropriate usage contexts.

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