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scan_project

Read-only

Analyze a project directory to identify its tech stack and get recommendations for MCP servers that integrate with it. Checks already installed servers to show what's missing.

Instructions

Scan a project directory to detect the tech stack and recommend MCP servers.

This is the best starting point. Analyzes project files (package.json, pyproject.toml, docker-compose.yml, .env, Dockerfile, etc.) to identify languages, frameworks, databases, and services in use, then recommends MCP servers that would be useful for that stack.

Results are cross-referenced with already-installed servers so you can see what's missing. Use configure_server to install recommended servers.

Args: path: Path to the project directory to scan. Defaults to current directory ("."). client: Which MCP client's config to check for already-installed servers. One of "claude_desktop", "claude_code", "cursor", "windsurf". Auto-detects if not provided.

Returns: Dict with: detected_technologies, env_vars_found, recommendations (each with already_installed flag), and a human-readable summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo.
clientNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description aligns with annotations (readOnlyHint=true) and explains the scan's behavior: analyzing files, cross-referencing with installed servers, and providing recommendations. No contradictions.

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

Conciseness5/5

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

The description is well-structured: a brief summary, then bulleted details on what it does, followed by explicit Args and Returns sections. Every sentence adds value, and the length is appropriate.

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

Completeness5/5

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

Given the tool's complexity (scans, cross-references, recommendations), the description covers all aspects: input parameters, return values (detected_technologies, recommendations with already_installed flag), and next steps. The presence of an output schema further reduces burden.

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

Parameters5/5

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

The description explains both parameters beyond the schema: 'path' defaults to current directory, 'client' auto-detects. With 0% schema coverage, the description fully compensates, adding meaning like 'auto-detects' which is not in the schema.

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 clearly states the tool's purpose: scanning a project directory to detect tech stack and recommend MCP servers. It differentiates itself from sibling tools by positioning as 'the best starting point' and specifies the types of files analyzed.

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 suggests this is the starting point and mentions using 'configure_server' to install recommendations. It provides clear context for when to use the tool, though it could be more explicit about when not to use it.

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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