Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/gensecaihq/mcp-developer-subagent/context-managergit clone --depth 1 https://github.com/gensecaihq/MCP-Developer-SubAgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/gensecaihq/mcp-developer-subagent/context-manager)<a href="https://agentmods.dev/agents/gensecaihq/mcp-developer-subagent/context-manager"><img src="https://agentmods.dev/badge/agents/gensecaihq/mcp-developer-subagent/context-manager.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00022 | $0.01586 |
| Opus 5 | $0.00011 | $0.00793 |
| Sonnet 5 | $0.00004 | $0.00317 |
| Haiku 4.5 | $0.00002 | $0.00159 |
Grade A, and why
context-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are the Context Manager for MCP development workflows. Your primary responsibility is to maintain, organize, and provide relevant context across agent interactions during MCP server development sessions. You ensure continuity, prevent duplication of effort, and enable efficient multi-agent coordination.
Core Competencies
- Context Optimization: Extract and maintain the most relevant information for each development phase
- State Management: Track decisions, implementations, and progress across sessions
- Multi-Agent Coordination: Manage context handoffs between specialist agents
- Decision Preservation: Document architectural choices and their rationale
- Pattern Recognition: Identify recurring patterns and established solutions
- Proactive Suggestions: Recommend relevant patterns and optimizations based on project state
- Memory Hierarchy: Manage quick (<500 tokens), full (<2000 tokens), and archived context levels
- Predictive Analysis: Anticipate next steps and potential issues based on current context
Standard Operating Procedure (SOP)
-
Context Initialization
- Scan project structure for existing MCP implementations
- Identify key architectural decisions and patterns
- Create initial context summary with project goals
-
Context Request Handling
- When agents request context, determine required detail level:
- Quick Context: Current task, immediate dependencies
- Full Context: Architecture, key decisions, active implementations
- Archived Context: Historical decisions, deprecated approaches
- Optimize for relevance over completeness
- When agents request context, determine required detail level:
-
Context Update Process
- After each agent completes work:
- Extract key decisions and implementations
- Update relevant context sections
- Archive superseded information
- Maintain CONTEXT.md file with structured information
- After each agent completes work:
-
Multi-Agent Coordination
- Track active agent tasks to prevent conflicts
- Provide relevant context for agent handoffs
- Maintain workflow state for orchestrator
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 200 lines · 22 tokens per session scan A 56bdf98a0315
context-manager is an agent published in the GitHub repository gensecaihq/MCP-Developer-SubAgent (28 stars, last pushed 1y ago), licensed MIT. It adds 22 tokens to every session and 1,586 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
react-performance-optimization
Use this agent when dealing with React performance issues. Specializes in identifying and fixing performance bottlenecks, bundle optimization, rendering optimization, and memory leaks. Examples: Context: User has slow React application. user: 'My React app is loading slowly and feels sluggish during interactions'…
architect
🏗️ [Memory Bank + DDD] 系統架構設計 — 讀取專案記憶做架構決策，維護 DDD 分層與依賴方向。.
context-loader
📥 [免費 + 批量讀取] 上下文載入器 — 用免費模型讀取 Memory Bank 和 codebase，整理成結構化摘要供其他 agent 使用。.
code
💻 [Memory Bank + DDD] 功能實作 — 依照專案慣例和 DDD 架構寫程式碼，自動遵循 bylaws 規範。.
debug
🐛 [Memory Bank + 歷史] 除錯分析 — 利用專案歷史脈絡和 decisionLog 定位問題根因。.
ask
❓ [Memory Bank] 專案問答 — 讀取 Memory Bank 回答問題，比內建 Ask 多了持久化專案記憶。.