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agentrix-ai

ClawdChat MCP Server

Official
by agentrix-ai

use_tools

Search and call over 80 MCP tools for tasks like search, GitHub, charts, and code execution. Rate tools and manage server connections.

Instructions

通过 ClawdChat 搜索和调用 80+ MCP 工具(搜索/GitHub/时间/图表/代码执行等)。 核心流程:搜索 tools → 读 inputSchema → 调用。 参数:

  • action: 操作类型

    • 'search': 搜索工具(需要 query 或 category 至少一个) · query 应匹配工具功能而非查询意图(如搜 'weather' 而非 '上海天气')

    • 'search_servers': 搜索 Server(需要 query 或 category)

    • 'categories': 列出所有工具分类

    • 'call': 调用工具(需要 server + tool_name,arguments 按 inputSchema 构造)

    • 'connect': 连接需要 OAuth 授权的 Server(需要 server)

    • 'rate': 使用后评分(需要 server + rating)

    • 'credits': 查看积分余额(每日免费 100 积分)

  • query: 搜索关键词(search/search_servers 时使用)

  • category: 工具分类(如 '搜索'、'开发'、'金融'、'社交')

  • server: Server 名称(call/connect/rate 时必填)

  • tool_name: 工具名称(call 时必填,从 search 结果获取)

  • arguments: 调用参数(call 时使用,必须严格按 inputSchema 构造)

  • rating: 评分 1-5(rate 时必填)

  • comment: 评分备注(rate 时可选)

  • search_mode: 搜索模式 keyword/semantic/hybrid(默认 hybrid)

  • limit: 搜索返回数量(默认 5,最大 15)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
queryNo
categoryNo
serverNo
tool_nameNo
argumentsNo
ratingNo
commentNo
search_modeNohybrid
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations exist, so description must disclose behavior. It explains the meta-tool nature, credit system, and argument construction, but lacks error handling or OAuth flow details.

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

Conciseness3/5

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

The description is fairly long but well-structured with bullet points for parameters. It is front-loaded with purpose, but some sentences are verbose and could be trimmed.

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?

For a meta-tool with 10 parameters and 7 actions, the description covers core functionality and parameter usage. It lacks examples of complete usage sequences, which would enhance completeness.

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

Parameters4/5

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

Schema coverage is 0%, so description must explain all parameters. It does so clearly, adding context like query matching tool function and argument construction rules. Some parameters like search_mode could use more detail.

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 it searches and calls 80+ MCP tools, listing specific actions. It is distinct from sibling tools which are individual tool functions.

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?

Provides explicit guidance for each action, including required parameters and workflow ('search tools → read inputSchema → call'). Lacks explicit when-not-to-use, but actions are well-defined.

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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