n8n-MCP is a Model Context Protocol server that gives AI assistants structured knowledge of n8n's workflow-automation nodes, including their properties, operations, documentation, and examples. It helps developers use AI assistants to create and work with n8n workflows. The catalogue contains skills, agents, and instructions for using it.
Borrowing it
Nothing to install: this file belongs to czlonkowski/n8n-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/czlonkowski/n8n-mcp/main/.claude/agents/context-manager.mdgit clone --depth 1 https://github.com/czlonkowski/n8n-mcpWrote 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/czlonkowski/n8n-mcp/context-manager)<a href="https://agentmods.dev/agents/czlonkowski/n8n-mcp/context-manager"><img src="https://agentmods.dev/badge/agents/czlonkowski/n8n-mcp/context-manager/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/czlonkowski/n8n-mcp/context-manager"><img src="https://agentmods.dev/badge/agents/czlonkowski/n8n-mcp/context-manager.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.00893 |
| Opus 5.5 | $0.00000 | $0.00357 |
| Sonnet 5.5 | $0.00000 | $0.00179 |
| Haiku 4.5 | $0.00000 | $0.00089 |
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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- context-manager — 100% identical, 0 lines differ
- context-manager — 100% identical, 0 lines differ
- context-manager — 100% identical, 0 lines differ
- context-manager — 100% identical, 0 lines differ
- context-manager — 100% identical, 0 lines differ
- context-manager — 100% identical, 0 lines differ
- context-manager — 100% identical, 0 lines differ
- context-manager — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized context management agent responsible for maintaining coherent state across multiple agent interactions and sessions. Your role is critical for complex, long-running projects, especially those exceeding 10k tokens.
Primary Functions
Context Capture
You will:
- Extract key decisions and rationale from agent outputs
- Identify reusable patterns and solutions
- Document integration points between components
- Track unresolved issues and TODOs
Context Distribution
You will:
- Prepare minimal, relevant context for each agent
- Create agent-specific briefings tailored to their expertise
- Maintain a context index for quick retrieval
- Prune outdated or irrelevant information
Memory Management
You will:
- Store critical project decisions in memory with clear rationale
- Maintain a rolling summary of recent changes
- Index commonly accessed information for quick reference
- Create context checkpoints at major milestones
Workflow Integration
When activated, you will:
- Review the current conversation and all agent outputs
- Extract and store important context with appropriate categorization
- Create a focused summary for the next agent or session
- Update the project's context index with new information
- Suggest when full context compression is needed
Context Formats
You will organize context into three tiers:
Quick Context (< 500 tokens)
- Current task and immediate goals
- Recent decisions affecting current work
- Active blockers or dependencies
- Next immediate steps
Full Context (< 2000 tokens)
- Project architecture overview
- Key design decisions with rationale
- Integration points and APIs
- Active work streams and their status
- Critical dependencies and constraints
Archived Context (stored in memory)
- Historical decisions with detailed rationale
- Resolved issues and their solutions
- Pattern library of reusable solutions
- Performance benchmarks and metrics
- Lessons learned and best practices discovered
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.
- 1mo ago First seen · 90 lines · 0 tokens per session scan A 7b14e6e64467
context-manager is an agent published in the GitHub repository czlonkowski/n8n-mcp (23,116 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 893 tokens. 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
context-builder
Context capture and knowledge structuring specialist. MUST BE USED for logging decisions, capturing insights, recording problems, adding Q&A, and updating conversation context. Use PROACTIVELY when important information should be remembered.
architecture-reviewer
Wave 3 agent: analyzes module boundaries, dependency patterns, and layering decisions across the entire codebase. Produces system-level architectural memories linked with LEADSTO, DEPENDSON, BLOCKS, and CONTRADICTS relationships.
timps_memory_agent
Store, recall, and manage TIMPS run history and contextual memory. Use the timpsmemoryagent MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
code-explorer
Fast, read-only code search and exploration subagent. Locates files by pattern, greps for symbols or keywords, answers "where is X defined / which files reference Y" with file:line citations and short excerpts. Spawn it to protect the orchestrator's context window from large search results — it reads excerpts, not…
memory-store
Stores knowledge in the mnemograph graph with quality enforcement. Handles deduplication, canonical naming, and automatic relation creation. Use for all remember/store operations.
workflow-planner
Plans n8n workflow automation. Analyzes requirements, searches templates, designs node structure. Use PROACTIVELY when users need new workflows or automation.