productivitydeveloper-tools

Tool Chainer

thirdstrandstudio

by thirdstrandstudio

Tool Chainer connects multiple MCP tools, streamlining complex workflows efficiently with reduced token usage.

Chains multiple MCP tools together in sequence, passing results between tools without sending large intermediate data back to the LLM, reducing token usage for complex workflows.

github stars

56

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Reduces token usage vs individual callsAutomatic tool discoveryJsonPath filtering support

best for

  • / Complex workflows requiring multiple tool calls
  • / Reducing token costs in multi-step processes
  • / Automating data processing pipelines
  • / Developers building MCP tool orchestration

capabilities

  • / Chain multiple MCP tools in sequence
  • / Pass results between tools using CHAIN_RESULT placeholder
  • / Filter data using JsonPath with inputPath/outputPath
  • / Discover available tools from configured MCP servers
  • / Execute complex workflows with minimal token usage

what it does

Chains multiple MCP tools together in sequence, passing results between them without sending large intermediate data back to the LLM to reduce token usage.

about

Tool Chainer is a community-built MCP server published by thirdstrandstudio that provides AI assistants with tools and capabilities via the Model Context Protocol. Tool Chainer connects multiple MCP tools, streamlining complex workflows efficiently with reduced token usage. It is categorized under productivity, developer tools.

how to install

You can install Tool Chainer in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Tool Chainer is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP Tool Chainer

Visit Third Strand Studio

An MCP (Model Context Protocol) server that chains calls to other MCP tools, reducing token usage by allowing sequential tool execution with result passing. Designed to solve https://github.com/modelcontextprotocol/specification/issues/215

image

Step function like JSON paths:

image

Features

  • Chain multiple MCP tools together in sequence
  • Pass results from one tool as input to another tool using CHAIN_RESULT placeholder
  • Filter and extract specific data using JsonPath with inputPath and outputPath parameters
  • Automatic tool discovery from configured MCP servers
  • Minimal token usage compared to individual tool calls

Tools

This server implements the following MCP tools:

  1. mcp_chain - Chain together multiple MCP servers
  2. chainable_tools - Discover tools from all MCP servers so the mcp_chain tool can be used
  3. discover_tools - Rediscover tools from all MCP servers

Installation

Prerequisites

  • Node.js (v16 or later)
  • npm

Installing from npm

# Install
npm install @thirdstrandstudio/mcp-tool-chainer

# Or use with npx directly
npx -y @thirdstrandstudio/mcp-tool-chainer

Installing from source

# Clone the repository
git clone https://github.com/thirdstrandstudio/mcp-tool-chainer.git
cd mcp-tool-chainer

# Install dependencies
npm install

# Build the package
npm run build

Usage with Claude Desktop, Cursor etc

ENSURE IT IS THE LAST MCP TO RUN OTHERWISE IT WILL HAVE TO RUN DISCOVERY AGAIN

Add the following to your claude_desktop_config.json or mcp.json:

If installed from npm globally

{
  "mcpServers": {
    "mcp_tool_chainer": {
      "command": "npx",
      "args": ["-y", "@thirdstrandstudio/mcp-tool-chainer", "`claude_desktop_config.json` or `mcp.json`"],
      "env": {}
    }
  }
}

If installed from source

{
  "mcpServers": {
    "mcp_tool_chainer": {
      "command": "node",
      "args": ["/path/to/mcp-tool-chainer/dist/index.js", "`claude_desktop_config.json` or `mcp.json`"],
      "env": {}
    }
  }
}

Replace /path/to/mcp-tool-chainer with the actual path to your repository.

image

Examples

Chain Browser and XPath Tools

// Fetch a webpage and then extract specific content with XPath
const result = await callTool("mcp_chain", { 
  "mcpPath": [
    {
      "toolName": "mcp_browser_mcp_fetch_url",
      "toolArgs": "{"url": "https://example.com"}"
    },
    {
      "toolName": "mcp_xpath_xpath",
      "toolArgs": "{"xml": CHAIN_RESULT, "query": "//h1"}"
    }
  ]
});

Using JsonPath with InputPath and OutputPath

// Fetch a webpage, extract specific content with XPath, then extract part of the result
const result = await callTool("mcp_chain", { 
  "mcpPath": [
    {
      "toolName": "mcp_fetch_fetch",
      "toolArgs": "{"url": "https://api.example.com/data"}"
    },
    {
      "toolName": "web_search",
      "toolArgs": "{"search_term": CHAIN_RESULT}",
      "inputPath": "$.results[0].title",     // Extract only the first result's title from previous output
      "outputPath": "$.snippets[*].text"     // Extract only the text snippets from the search results
    },
    {
      "toolName": "another_tool",
      "toolArgs": "{"content": CHAIN_RESULT}"
    }
  ]
});

JsonPath Support

MCP Tool Chainer now supports AWS Step Functions-style InputPath and OutputPath features:

  • inputPath: JsonPath expression to extract specific portions of the input before passing to a tool
  • outputPath: JsonPath expression to extract specific portions of the output before passing to the next tool

These features work only when the input/output is valid JSON. If JsonPath extraction fails, the original input/output is used.

For JsonPath syntax reference, see JsonPath Syntax.

Benefits

  • Reduced Token Usage: By chaining tools together, you avoid sending large intermediate results back to the LLM
  • Simplified Workflows: Create complex data processing pipelines with a single tool call
  • Improved Performance: Reduce latency by minimizing round-trips between the LLM and tools
  • Precise Data Flow Control: Extract only the data you need with JsonPath expressions

Development

# Install dependencies
npm install

# Start the server
node dist/index.js config.json

# List available tools
node dist/index.js config.json discover_tools

License

This MCP server is licensed under the MIT License.


Created by Third Strand Studio

FAQ

What is the Tool Chainer MCP server?
Tool Chainer is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Tool Chainer?
This profile displays 51 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.751 reviews
  • Ren Sethi· Dec 28, 2024

    We evaluated Tool Chainer against two servers with overlapping tools; this profile had the clearer scope statement.

  • Ganesh Mohane· Dec 24, 2024

    Tool Chainer is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Lucas Gupta· Dec 20, 2024

    Tool Chainer reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Lucas Mehta· Dec 4, 2024

    Tool Chainer is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Lucas Robinson· Nov 19, 2024

    I recommend Tool Chainer for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Mei Agarwal· Nov 11, 2024

    Useful MCP listing: Tool Chainer is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Ren Jain· Oct 10, 2024

    We evaluated Tool Chainer against two servers with overlapping tools; this profile had the clearer scope statement.

  • Lucas Verma· Oct 6, 2024

    Tool Chainer reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Henry Desai· Oct 2, 2024

    We evaluated Tool Chainer against two servers with overlapping tools; this profile had the clearer scope statement.

  • Luis Iyer· Sep 25, 2024

    According to our notes, Tool Chainer benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

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