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probe_scan

Scan a directory to discover AI tools, MCP servers, agent frameworks, API keys, and local models. Supports full, quick, or category-specific scans.

Instructions

Scan the current environment for AI tools, MCP servers, agent frameworks, API keys, and local models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoRoot path to scan (defaults to current directory)
scopeNoScan scope: full=everything, quick=IDE+MCP only, category=specificfull
categoriesNoComma-separated categories: ide,mcp,agents,keys,models,deps
includeProjectDepsNoScan package.json/requirements.txt for AI dependencies
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. The word 'Scan' suggests a read operation, but there is no mention of side effects, permissions, safety, or potential impact on the environment. For a tool that scans files and possibly accesses sensitive data (API keys), this is a significant omission.

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 a single sentence that front-loads the action and key details. Every word contributes to understanding the tool's purpose, with no filler or redundancy.

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

Completeness2/5

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

With no output schema and 4 optional parameters, the description fails to cover what the tool returns (e.g., a list of found items, JSON output). The agent lacks information on how to interpret results, which is critical for a scanning tool. Additionally, it does not explain the behavior of different scopes or categories beyond what the schema provides.

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 does not add any additional meaning beyond the schema; it simply restates the categories listed in the 'categories' parameter description. No deeper semantics are provided.

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 action ('Scan') and the specific resources ('AI tools, MCP servers, agent frameworks, API keys, local models'), making it easy for an AI agent to understand the tool's purpose. No sibling tools exist, so differentiation is not required.

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?

While there are no sibling tools to compare against, the description implies the tool is for enumeration and discovery, which is sufficient. However, it lacks explicit guidance on when to use it (e.g., initial reconnaissance vs. targeted search), leaving some ambiguity.

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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