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get_by_platform

Find AI agent tools for your platform. Specify your agent or editor (Claude, Cursor, VS Code, etc.) to receive a ranked list of compatible tools.

Instructions

List the top AI agent tools that work on a specific platform (Claude, ChatGPT, Cursor, Windsurf, Cline, VS Code, JetBrains, OpenClaw, opencode, Ollama, LM Studio, local/open-source). Use this when the user runs a specific agent or editor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results (1-20, default 10)
platformYesThe agent platform the tool must support
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only says 'top' without explaining how ranking is determined, and it omits details about return format, pagination, or any access requirements. The description is essentially just a purpose statement with no additional behavioral traits.

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

Conciseness4/5

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

The description is front-loaded with the action and resource, and the second sentence provides clear usage context. The parenthetical platform list is somewhat long but informative. Overall, there is no fluff, but a more compact structure could reduce redundancy with the schema enum.

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

Completeness3/5

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

The tool is simple with only two parameters, and the description covers the core purpose and usage. However, with no output schema and no annotations, it does not describe the response shape or the meaning of 'top,' leaving an agent to infer those details. It is minimally viable but lacks comprehensive context.

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 adds human-readable platform examples (e.g., 'local/open-source') and suggests the filter semantics, but it does not add new technical meaning beyond the schema's enum and limit description.

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 uses the specific verb 'List' and clearly identifies the resource: 'top AI agent tools that work on a specific platform.' It distinguishes itself from siblings by focusing on platform-specific filtering, and the usage phrase 'when the user runs a specific agent or editor' further clarifies its niche.

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 explicitly states when to use the tool: 'Use this when the user runs a specific agent or editor.' However, it does not discuss when not to use it or name alternatives such as search_tools or get_top_rated, so it stops short of full exclusions.

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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