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Agent Earth

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infometa
agent-earth

AgentEarth external tool marketplace skill — discover and execute 1400+ external API tools (weather, finance, search, maps, AI generation, and more) via the AgentEarth MCP connector.

Overview

Publisherinfometa
Repositoryworkbuddyskills
Skill nameagent-earth
Stars
376
Forks
124
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by infometa on GitHub. Read the source before you install it.

Installation

Install the Agent Earth AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/infometa/workbuddyskills/tree/main/connectors/agent-earth/skills
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/infometa/workbuddyskills.git /tmp/workbuddyskills
mkdir -p .claude/skills
cp -r /tmp/workbuddyskills/connectors/agent-earth/skills .claude/skills/agent-earth
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Earth in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Earth on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Earth is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AgentEarth Skill

This connector exposes 5 MCP tools backed by the AgentEarth platform. AgentEarth gives access to 1400+ external API tools across categories including AI generation, web search, maps, weather, finance, media metadata, developer APIs, and news. Use these MCP tools directly — do not call any HTTP endpoint yourself; the connector already handles authentication.

Available Tools

GetAccountOverview

Returns the authenticated user's account info. Takes no arguments.

Response fields: user_id, user_name, key_name, credit (remaining balance), error_no (0 = success).

Call this directly — without RecommendTools first — whenever the user asks about their AgentEarth account, user ID, API key name, or credit balance.

RecommendTools - discover tools for a task

参数类型必填说明
querystring是面向任务的自然语言描述
limitinteger-返回工具数量上限 (1-50),默认 5

Returns a list of candidate tools, each with:

  • tool_name — the tool's identifier
  • tool_url — opaque URL to pass to ExecuteTool; never modify it
  • description, when_to_use — what the tool does and when to pick it
  • credit — cost of one call, use this plus task fit to choose among candidates
  • input_schema — JSON Schema for ExecuteTool's params
  • associated_tools — optional companion tools (e.g. a geocoding/ID lookup tool) that may need to be called first if a required field can't be filled directly from user input

Call this before ExecuteTool for any external-tool task. Read every candidate's input_schema and associated_tools before picking one — some fields require an ID or code obtained from a companion lookup tool rather than free text (e.g. a weather tool that needs a location ID resolved by a geocoding tool first).

ListTools - browse tools without semantic search

参数类型必填说明
pageinteger-页码,从 1 开始,默认 1
page_sizeinteger-每页数量 (1-100),默认 20
keywordstring-按名称/描述关键词过滤
sortstring-排序方式

Use when the user wants to browse or search tools by name/category rather than describe a task.

GetToolDetail - inspect one tool by exact name

参数类型必填说明
tool_namestring是精确工具名

Returns the same shape as one entry from RecommendTools/ListTools (tool_url, description, input_schema, etc). Use when you already know the exact tool_name (e.g. from associated_tools, or the user named a specific tool) and don't need semantic search.

ExecuteTool - run a tool

参数类型必填说明
tool_urlstring是从 RecommendTools/GetToolDetail 原样取得的 URL,禁止修改、拼接或重排
paramsobject-按所选工具 input_schema 构造的入参

Must be called after RecommendTools or GetToolDetail. tool_url must be passed through byte-for-byte as returned — do not edit, re-encode, or reconstruct it. Build params strictly from the selected tool's input_schema.properties; validate required, type, enum, and additionalProperties before calling.

Examples

  • "What's my AgentEarth credit balance?" → call GetAccountOverview() directly (no RecommendTools).
  • "Use AgentEarth to check today's weather in Shanghai" → RecommendTools({"query": "current weather in Shanghai"}) → pick a weather tool from the candidates → if its input_schema needs a location ID/adcode rather than a free-text city name, call the companion lookup tool named in associated_tools first → ExecuteTool({"tool_url": <candidate's tool_url>, "params": {...}}).
  • "Use AgentEarth to check the current Bitcoin price" → RecommendTools({"query": "current bitcoin price"}) → ExecuteTool with the chosen finance tool's tool_url and schema-derived params.
  • "List the AgentEarth tools related to maps" → ListTools({"keyword": "map"}) to browse by name instead of describing a task.
  • User names a specific tool exactly (e.g. "run ae_qweather_geo_top_city") → GetToolDetail({"tool_name": "ae_qweather_geo_top_city"}) instead of RecommendTools.

Workflow

  1. Account questions (user ID, user name, key name, credit balance) → call GetAccountOverview directly. Skip RecommendTools for these.
  2. External-tool tasks → call RecommendTools with a natural-language description of the task, or ListTools/GetToolDetail when the user wants to browse or inspect tools by name.
  3. Review the candidates' tool_url, description, when_to_use, and input_schema. Pick the best fit by task relevance, schema clarity, and credit cost; keep a fallback candidate when one exists.
  4. If a required param needs an ID/code from a companion tool (see associated_tools), call that tool first.
  5. Call ExecuteTool with the exact tool_url and params built from input_schema.
  6. Never invent a required value (URL, ID, token, code snippet, or other concrete input) that the user hasn't provided — ask the user instead.

Reading the Result

ExecuteTool's MCP result has an isError field — that is the reliable success/failure signal, not a fixed field inside the payload:

  • isError absent/false — the call succeeded. The content is the raw response of the underlying third-party API, in that API's own format (it may or may not contain something resembling error_no/status fields of its own — don't assume a single universal shape across tools).
  • isError: true — the call failed. The error detail comes back wrapped as <tool_output>...</tool_output>, often followed by a <platform_annotation> with a suggestion (e.g. "revise your request parameters based on the upstream provider's error message"). Use that detail to correct params and retry, or fall back to another candidate from RecommendTools if the issue isn't fixable from user-provided input.

GetAccountOverview and GetToolDetail/RecommendTools/ListTools (the AgentEarth-native lookups, as opposed to ExecuteTool passthrough results) do use error_no == 0 as their own success signal.

Credentials

The AgentEarth API key is injected by WorkBuddy from the connector's Token form (token-schema.json) directly into the MCP connection — this skill never sees or handles it. Do not ask the user to paste an API key in chat. If the connector shows as disconnected or a call fails with an auth error, tell the user to (re)connect AgentEarth from the WorkBuddy Connector page.

If the user needs a new or replacement AgentEarth API key (lost, revoked, or first-time setup), point them to https://agentearth.ai/r/8oo9zmDn — sign in, then open the avatar menu in the top-right corner and go to API Keys — then have them paste it into the WorkBuddy Connector's Token form, never into the chat.

Incorrect Flow

  • Calling ExecuteTool before RecommendTools/GetToolDetail.
  • Modifying, re-encoding, or reconstructing tool_url.
  • Inventing required params instead of asking the user.
  • Treating error_no == 0 as the success signal for ExecuteTool results — use isError instead.
  • Making raw HTTP requests to AgentEarth endpoints yourself, or asking the user for their API key — this connector only ever talks to AgentEarth through the 5 MCP tools above.

Frequently asked questions

What does the Agent Earth AI skill do?

AgentEarth external tool marketplace skill — discover and execute 1400+ external API tools (weather, finance, search, maps, AI generation, and more) via the AgentEarth MCP connector.

Why use Agent Earth on TypingMind?

Because you install it once and use it with any model. Agent Earth is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Earth in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/infometa/workbuddyskills/tree/main/connectors/agent-earth/skills. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Earth?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Earth?

As many as you like. As long as a model supports skills, you can use Agent Earth with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Earth AI skill free?

It is published on GitHub by infometa. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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