Reading
Through on-demand tool discovery and phased mandatory workflows, jig reduces the tool schema tokens at MCP session startup from 17K to 2K, cutting context overhead by 88%. It supports semantic search for on-demand invocation, shadow branch snapshots, and agent deployment orchestration.
Section 01
Jig is a dynamic discovery and phased workflow framework for MCP tools targeting AI coding agents. Its core reduces tool schema tokens at MCP session startup from 17K to 2K (cutting context overhead by 88%) through on-demand tool discovery and phased mandatory workflows. It supports features like semantic search for on-demand invocation, shadow branch snapshots, and agent deployment orchestration.
Section 02
Model Context Protocol (MCP) is a standard protocol for AI agents to interact with external tools, but tool bloat has become a prominent issue: Full loading of tool schemas consumes about 17,000 tokens at each session startup (7 MCP servers ×30 tools/server ×80 tokens/tool), leading to problems like context window waste, increased latency, cognitive overload, and higher costs.
Section 03
Jig uses a single entry point architecture, acting as the only registered MCP server to proxy all external tools. Tools are divided into two layers: hot path (26 core tools) and on-demand proxy (30+ internal domain operations). Internal functions are organized into 8 domains like graph, snapshot, and experience.
Section 04
The embedding model BAAI/bge-large-en-v1.5 is used, with fastembed generating embeddings cached in SQLite. Natural language tool lookup is implemented via the proxy_tools_search interface.
Any MCP tool can be uniformly called via execute_mcp_tool(mcp, tool, args).
Automatically captured after each Edit/Write/Bash operation (30-second throttling), stored in orphan commits under refs/jig/snapshots/, with good isolation and additional code smell analysis.
Section 05
Phase0 (jig_init_project): Copy templates, migrate MCP, inject methodology, configure editors; Phase1 (deploy_project_agents): Orchestrate core agents, select dedicated agents, inject skills, customize rules.
Define tools_blocked, mcps_enabled, and tension_gate using YAML, and enforce phase processes via PreToolUse/PostToolUse hooks.
Section 06
| Metric | Traditional Approach | Jig Approach | Optimization Rate |
|---|---|---|---|
| Number of Startup Tools | 210 | 26 | -88% |
| Startup Token Count | ~17,000 | ~2,000 | -88% |
| MCP Registration Count | 7 | 1 | -86% |
Installation: uv tool install git+https://github.com/Rixmerz/jig; Configuration is simplified to a single Jig entry; Upgrade: uv tool upgrade jig-mcp.
High-frequency sessions, large-scale project development, team collaboration standardization, methodology precipitation.
Section 07
PyPI release, multi-language support, configurable embedding model, IDE integration.
Section 08
Jig elegantly solves the MCP tool bloat problem through its three-in-one design of single entry point, on-demand discovery, and phase gating. The 88% reduction in startup overhead comes from intelligent layered exposure and semantic organization. It has reference value for systems facing full-loading dilemmas, and MCP developers are recommended to evaluate and use it.
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