Identify project types in a directory by scanning for marker files like pyproject.toml, package.json, or Cargo.toml, so you can tailor builds, dependencies, and tooling.
Guess the project type(s) present in a directory by looking for common marker files (pyproject.toml, package.json, Cargo.toml, etc.).
Args: path: Directory to inspect, relative to the workspace root.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | . |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the detection method (marker-file scanning) but says nothing about permissions, what happens when no markers are found, or the shape of results (a list? confidence scores?). For a read-only detection tool this is thin.
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?
Front-loaded with the core purpose in one tight sentence, followed by the parameter note. No filler, though the two-line 'Args' block is slightly redundant formatting for a single parameter.
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?
With no annotations and no output schema, the description should explain the return value (e.g., a list of detected project types) since nothing else does. It covers the input adequately but leaves the output shape entirely unspecified.
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 coverage is 0% and the schema only names the parameter, so the description must compensate. It adds real meaning: the path is 'relative to the workspace root', which clarifies the resolution base beyond the bare 'default: .' in the schema.
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?
States a specific verb ('Guess'/'detect') and resource (project type(s) in a directory) plus the mechanism used (marker files like pyproject.toml, package.json). No sibling tool overlaps, so an agent can route to it unambiguously.
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 implies the scenario (inspecting a directory to identify its project type) but never states when to prefer this over alternatives or any prerequisites. No sibling performs detection, so there is little to contrast, but no explicit guidance is given either.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.