> ## Documentation Index > Fetch the complete documentation index at: https://docs.junis.ai/llms.txt > Use this file to discover all available pages before exploring further. # Tools Overview > Extend your agents with powerful tools and capabilities ## What Are Tools in Junis? Tools are **specialized functions** that agents can call to perform specific actions or access external data. They extend your agent's capabilities beyond text generation, enabling real-world interactions. Think of tools as **skills** your agents can learn: * 🔍 **Search tools**: Find information from web, databases, or documents * 📄 **Document tools**: Process PDF, Excel, Word, PowerPoint files * 🔌 **External connectors**: MCP, Composio OAuth (200+ apps), GitHub, Firecrawl * 🧠 **RAG tools**: Search and create knowledge base documents with AI-powered retrieval * 💾 **Memory tools**: Read and update persistent user memory across conversations * ✨ **Soul tools**: Read and update organization AI personality * 📚 **Wiki tools**: Read and grow your organization's shared knowledge base (second brain) * 🤝 **Squad tools**: Discover and message other squads you have access to * 🖥️ **Code execution**: Run code in an isolated cloud sandbox or on your own connected device **Agent Intelligence**: Agents automatically decide when and how to use tools based on user requests. You don't need to manually trigger them. *** ## Tool Types Junis supports four types of tools, each designed for specific use cases: **Python Functions** Custom async functions you build for your specific needs. *Examples*: Search databases, call APIs, process data **Official Anthropic Tools** Prebuilt document processors powered by Claude. *Examples*: PDF extraction, Excel analysis, PPTX creation **Third-Party Integrations** Connect to external systems and services. *Examples*: Salesforce, DataLab, GitHub, Firecrawl (via MCP) **Document Retrieval** Auto-generated search tools for uploaded documents. *Examples*: Company policies, employee handbooks, product specs *** ## How Agents Use Tools ### 🤖 Automatic Tool Selection Agents use tools intelligently based on: 1. **User Request**: What the user is asking for 2. **Tool Descriptions**: What each tool can do (from docstrings) 3. **Conversation Context**: Previous messages and results **Example Workflow**: ``` User: "Find the latest news about artificial intelligence" ↓ Agent thinks: - User wants to search the web for current information - I have a tool called "google_search" - Its description says it searches the web for real-time information - I should use this tool with query="latest artificial intelligence news" ↓ Agent calls: google_search("latest artificial intelligence news") ↓ Tool returns: { "results": [ { "title": "AI Breakthrough in 2026", "url": "https://example.com/ai-news", "snippet": "Recent developments in AI technology..." } ] } ↓ Agent responds: "I found recent AI news. Here are the latest developments: AI Breakthrough in 2026..." ``` **No Manual Intervention**: Once tools are configured, agents use them automatically without user or admin prompting. ### 📊 Tool Execution Flow ```mermaid theme={null} sequenceDiagram participant User participant Agent participant Tool Registry participant External System User->>Agent: "Analyze this PDF document" Agent->>Tool Registry: Check available tools Tool Registry-->>Agent: claude_pdf_processor Agent->>Tool: claude_pdf_processor(file_url, instruction) Tool->>External System: Claude Skills API External System-->>Tool: Extracted text + analysis Tool-->>Agent: Result object Agent-->>User: "The document contains 15 pages and discusses..." ``` *** ## Available Built-in Tools Junis provides a comprehensive set of prebuilt tools ready to use: ### Document Processing (Claude Skills) **`claude_pdf_processor`** * Extract text from PDFs * Extract tables and data * Create new PDFs * Merge multiple PDFs * Convert to images *Use Case*: "Extract all customer information from this invoice PDF" **`claude_xlsx_processor`** * Analyze spreadsheets * Extract specific columns/rows * Generate charts * Perform calculations * Export filtered data *Use Case*: "Calculate total revenue from this sales spreadsheet" **`claude_pptx_creator`** * Create presentations * Add slides with content * Insert images and charts * Apply templates * Export to PDF *Use Case*: "Create a 5-slide presentation about our Q4 results" **`claude_docx_processor`** * Extract text from Word docs * Create new documents * Track changes * Manage comments * Convert to other formats *Use Case*: "Extract all action items from this meeting notes document" ### Search Tools **`google_search`** Search the web for real-time information using Google. **Parameters**: * `query` (str): Search query **Returns**: Search results with titles, URLs, and snippets **Use Case**: "Find the latest AI news" or "Search for Python tutorials" ### Built-in Web Search (LLM Feature) **NEW**: Agents can now perform web searches automatically during response generation without explicitly calling a tool. **Enable Web Search** in Admin > Agents > Edit Agent: ```yaml theme={null} Agent Configuration: Enable Web Search: ✅ ON ``` **How It Works**: * When enabled, the LLM can automatically search the web during response generation * No explicit tool call required - the model decides when to search * Uses LiteLLM's `web_search_options` with `search_context_size: "medium"` **Difference from google\_search Tool**: | Feature | Built-in Web Search | google\_search Tool | | ----------------- | ------------------------- | -------------------- | | **Activation** | Automatic (LLM decides) | Explicit tool call | | **Configuration** | Toggle in Agent settings | Assign tool to agent | | **Best For** | General knowledge queries | Targeted searches | **When to Use**: Enable for agents that need to answer questions about current events, recent news, or real-time information without explicit search requests. ### Memory & Soul Tools (Auto-Injected) **Automatic**: These tools are automatically available to all orchestrator agents. No configuration needed. **`update_user_memory(action, content)`** Read or update the user's persistent profile (name, role, preferences). **Actions**: * `read`: View the user's current profile * `update`: Save updated profile content *Use Case*: Agent learns user's name, role, or communication preferences during conversation **`save_to_eternal_memory(action, content)`** Store permanent facts and standing instructions that persist across all conversations. **Actions**: * `read`: View stored eternal memories * `add`: Append a new memory entry * `update`: Replace all eternal memory content *Use Case*: User says "remember that I always want bullet points" or "don't forget my deadline is Friday" **`update_soul(action, content)`** Read or update the organization's AI thinking philosophy (Admin/Owner only). *Use Case*: Admin asks "update our AI to prioritize data-driven decisions" **`update_identity(action, content)`** Read or update the organization's AI communication style (Admin/Owner only). *Use Case*: Admin asks "make our AI more casual and friendly" Learn more about these systems: [Memory System](/guides/memory-system) | [Soul & Identity](/guides/soul-identity) ### Organization Wiki Tools (Auto-Injected) **Automatic**: Wiki tools are available to all orchestrator agents with no configuration. Every member's agent can read and grow the organization's shared knowledge base. | Tool | What It Does | | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | `wiki_list` | Reads the wiki index — every document with its one-line summary. Agents check this before answering to see if your team already knows the answer | | `wiki_read` | Reads one document's full content | | `wiki_create` | Saves new knowledge as a document ("save this to the wiki") | | `wiki_update` | Replaces a document's content entirely | | `wiki_edit` | Makes a precise partial edit without touching the rest | | `wiki_append` | Adds entries to an existing document over time (FAQ items, meeting notes) | There is **no delete tool** by design — documents can only be deleted by admins in **Team → Wiki**. See the [Organization Wiki guide](/guides/wiki-second-brain) for details. ### Squad Collaboration Tools (Auto-Injected) **Automatic**: Every orchestrator can discover and message other squads you have access to. | Tool | What It Does | | -------------------- | --------------------------------------------------------------------------------------------- | | `squad_list` | Lists the squads you can reach — your active memberships plus public squads | | `call_another_squad` | Sends a message to another squad's orchestrator and returns its answer into your current chat | See [Squad Collaboration](/guides/orchestration/squad-collaboration) for access rules, billing, and chain limits. ### Code Execution (Cloud Sandbox & Your Device) Agents can run code and shell commands in two very different places: Code-execution **Skills** run in an isolated cloud sandbox — no access to your computer, isolated per organization and session. Output streams into the chat in real time. With the [Desktop Agent (`npx junis`)](/guides/npx-junis), agents can work directly on **your own computer** — files, shell commands, browser automation — with live output in chat. Rule of thumb: use the cloud sandbox for safe, disposable code runs; connect a device when the work needs **your** files and apps. ### RAG Document Upload (Agent Tool — Requires Configuration) **`upload_rag_document(datastore_name, file_name, content, description?)`** Agents can **generate and store their own knowledge base documents** as Markdown files. This is unique to Junis — your AI agents can research, synthesize, and save information for future retrieval. **Configuration Required**: Unlike memory and soul tools (which are auto-injected), `upload_rag_document` must be explicitly assigned to an agent via **Admin > Agents > Edit Agent > Tools**. **How it works**: 1. Agent generates content (e.g., research summary, analysis report) 2. Agent calls `upload_rag_document` with the content as Markdown 3. A `.md` file is created, uploaded to a RAG DataStore, and indexed 4. Future conversations can search and retrieve this knowledge **Use Case**: "Research the latest AI trends and save a summary to our knowledge base" ``` Agent: 1. Searches the web for AI trends 2. Synthesizes findings into a structured document 3. Calls upload_rag_document( datastore_name="Research Library", file_name="ai-trends-q1-2026", content="# AI Trends Q1 2026\n\n## Key Developments..." ) 4. Document is indexed and searchable by all agents ``` **DataStore Auto-Creation**: If the specified DataStore doesn't exist, the tool creates it automatically. The file extension `.md` is auto-appended if not provided. ### Payment Tools (AP2/x402 Protocol) **Premium Feature**: Payment tools enable USDC-based agent usage fees with 80/20 revenue split (Agent Owner 80%, Platform 20%). **`require_payment(agent_id, network?)`** Request payment before agent execution. **When to Use**: * Premium agents that charge per use * Computationally expensive operations * Third-party API costs **Returns**: * Payment requirements (amount, wallet, network) * EIP-3009 nonce for signature * 80/20 split details **`verify_payment(nonce, payment_type)`** Verify EIP-3009 payment signatures. **Payment Types**: * `agent_owner`: 80% to agent owner * `platform_fee`: 20% to platform **Auto-Settlement**: When both payments verified, automatically settles on-chain. *** ## When to Use Tools ### ✅ Use Tools When You Need To: * **Access External Data**: Databases, APIs, file storage * **Perform Complex Operations**: File processing, calculations, analysis * **Integrate Third-Party Services**: Salesforce, GitHub, Firecrawl * **Retrieve Uploaded Documents**: RAG search through company docs ### ❌ Don't Build Tools For: * **Simple Text Generation**: Agents already excel at this * **One-Time Tasks**: Better to do manually * **Highly Variable Logic**: Better in agent instructions * **Duplicate Functionality**: Use existing tools or MCP platforms *** ## Getting Started with Tools ### Step 1: Choose Your Approach **Fastest**: Junis provides 15+ prebuilt tools. 1. Go to **Admin > Agents** 2. Edit your agent 3. Select tools from the Tools section 4. Save Done! Your agent can now use those tools. **Recommended**: Use Model Context Protocol for external services. 1. Go to **Team > MCP** 2. Connect platforms (GitHub, Firecrawl, etc.) 3. Assign to agents See [MCP Integration Guide](/guides/mcp/overview) for details. **For Advanced Use Cases**: Junis supports custom tool development for specialized business logic that isn't covered by built-in tools. **Process**: 1. **Contact Junis Support** at [contact@junis.ai](mailto:contact@junis.ai) 2. Describe your use case and requirements 3. Our team will develop and register the custom tool 4. You assign it to your agents via Admin UI **Note**: Most users don't need custom tools - the built-in library covers common use cases. **For Knowledge Bases**: Upload PDFs, docs, etc. 1. Go to **Team > RAG Knowledge** 2. Create or select DataStore 3. Upload files 4. Assign RAG tool to agent See [RAG Guide](/guides/tools/rag) for details. ### Step 2: Configure Agent Tools **Admin Dashboard** → **Agents** → **Edit Agent** ```yaml theme={null} Agent Configuration: Name: Customer Support Agent Type: LLM_AGENT Tools: Regular Tools: - claude_pdf_processor - google_search RAG Tools: - rag_company-policies_1234567890 MCP Platforms: - GitHub (for repository management) - Firecrawl (for web scraping) ``` ### Step 3: Test with Chat ``` You: "Search for recent AI trends and create a GitHub issue to track them" Agent: 1. Uses google_search("recent AI trends 2026") 2. Formats the results 3. Uses GitHub MCP to create issue Response: "I found the latest AI trends and created a tracking issue in your repository." ``` *** ## Tool Best Practices **✅ DO**: * Choose specific tools for specific tasks * Write clear tool descriptions (docstrings) * Test tools individually before assigning to agents * Monitor tool usage in logs * Handle errors gracefully * Use RAG for frequently accessed documents **❌ DON'T**: * Add too many tools (max 10 per agent recommended) * Use tools with unclear purposes * Forget to update tool descriptions * Ignore tool errors in production * Hard-code sensitive data in tools *** ## Real-World Examples ### 1. Customer Support Agent **Tools**: * `claude_pdf_processor`: Extract info from uploaded docs * GitHub MCP: Create support tickets as issues * `rag_faq_documents`: Search company FAQ **User**: "A customer asked about our return policy" **Agent Flow**: 1. Search `rag_faq_documents` for "return policy" 2. Extract relevant sections 3. Format a friendly response 4. Optionally create GitHub issue for tracking ### 2. Data Analysis Agent **Tools**: * `claude_xlsx_processor`: Analyze Excel files * `google_search`: Find market trends and research * `claude_pptx_creator`: Generate presentation **User**: "Analyze Q4 sales and create a presentation" **Agent Flow**: 1. Use `claude_xlsx_processor` to analyze sales spreadsheet 2. Use `google_search` for market research and context 3. Use `claude_pptx_creator` to generate slides ### 3. Premium Agent with Payments **Tools**: * `require_payment`: Gate agent access with USDC payment * `claude_pdf_processor`: Document processing * `google_search`: Research capabilities **User**: "Run premium analysis workflow" **Agent Flow**: 1. Call `require_payment(agent_id="premium_agent")` 2. Wait for user payment (MetaMask) 3. Verify payment signatures 4. Proceed with premium analysis using available tools *** ## Tool Categories Reference | Category | Tools | Primary Use | | ----------------------- | ----------------------------------------------------------------------------------------------- | --------------------------------------- | | **Document Processing** | claude\_pdf\_processor, claude\_xlsx\_processor, claude\_pptx\_creator, claude\_docx\_processor | File analysis, creation, extraction | | **Web Search** | google\_search, built-in web search | Real-time web information retrieval | | **Memory** | update\_user\_memory, save\_to\_eternal\_memory | Persistent user memory management | | **Soul & Identity** | update\_soul, update\_identity | Organization AI personality | | **Organization Wiki** | wiki\_list, wiki\_read, wiki\_create, wiki\_update, wiki\_edit, wiki\_append | Shared team knowledge (second brain) | | **Squad Collaboration** | squad\_list, call\_another\_squad | Cross-squad questions and workflows | | **Code Execution** | code-execution skills (cloud sandbox), Desktop Agent tools | Run code in the cloud or on your device | | **RAG Search** | rag\_{datastore_id} (auto-generated) | Document knowledge retrieval | | **RAG Upload** | upload\_rag\_document | Agent-generated knowledge creation | | **Payment** | require\_payment, verify\_payment | Premium agent gating | | **External** | MCP platforms, Composio OAuth | Third-party service integration | *** ## Performance Considerations ### ⚡ Tool Execution Speed | Tool Type | Typical Latency | Notes | | ------------------ | --------------- | ------------------------------------ | | **Database Query** | 100-500ms | Depends on query complexity | | **External API** | 1-5 seconds | Network dependent | | **Claude Skills** | 2-10 seconds | File processing time | | **RAG Search** | 1-3 seconds | Vertex AI Search latency | | **Payment** | 5-30 seconds | User signature + on-chain settlement | **Optimization Tip**: Group multiple simple queries into batch tools rather than making agents call tools repeatedly. *** ## Troubleshooting **Symptom**: Tool is configured but agent doesn't call it **Possible Causes**: * Tool description (docstring) unclear * Agent instruction doesn't mention tool usage * Tool name not intuitive **Solution**: 1. Review tool docstring for clarity 2. Add tool usage examples to agent instruction 3. Test with explicit request: "Use \[tool\_name] to..." **Symptom**: Tool called but no result returned **Possible Causes**: * Tool has unhandled exception * Missing required parameters * External service down **Solution**: 1. Check agent session logs for errors 2. Test tool directly via API 3. Verify all environment variables set 4. Check external service status **Symptom**: RAG search completes but finds nothing **Possible Causes**: * Documents not indexed yet (still processing) * Search query too specific * DataStore not created properly **Solution**: 1. Go to **Team > RAG Knowledge** and check indexing status 2. Wait for "COMPLETED" status (can take 5-60 minutes) 3. Try broader search terms 4. Re-upload documents if status is "FAILED" **Symptom**: require\_payment call takes too long **Possible Causes**: * Network congestion (Base blockchain) * CDP Facilitator API slow * User hasn't signed MetaMask prompt **Solution**: * Payment tools have 30-second timeout * Check Base network status: [https://status.base.org](https://status.base.org) * Ensure user completes MetaMask signature promptly * For testnet, use Base Sepolia (faster, free) *** ## What's Next? Persistent AI memory across conversations Define your AI team's personality Upload documents and create knowledge bases Connect external services via MCP and Composio *** ## Additional Resources * **Claude Skills**: [https://docs.anthropic.com/en/docs/build-with-claude/tool-use](https://docs.anthropic.com/en/docs/build-with-claude/tool-use) - Official Anthropic documentation on tool use **Need Help?** Contact us at [contact@junis.ai](mailto:contact@junis.ai) for assistance with tool development and custom implementations.