Context Engineering Template is a repository of instructions, examples, workflows, and validation practices that give AI coding assistants the information they need to complete software tasks. It is for developers working with Claude Code or other coding assistants, and the catalogue entries package parts of its workflow as commands, agents, instructions, and a skill.
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 commands/coleam00/context-engineering-intro/prp-mcp-creategit clone --depth 1 https://github.com/coleam00/context-engineering-introWrote 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/commands/coleam00/context-engineering-intro/prp-mcp-create)<a href="https://agentmods.dev/commands/coleam00/context-engineering-intro/prp-mcp-create"><img src="https://agentmods.dev/badge/commands/coleam00/context-engineering-intro/prp-mcp-create.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 5.1 | $0.00046 | $0.00947 |
| Opus 5 | $0.00023 | $0.00474 |
| Sonnet 5 | $0.00009 | $0.00189 |
| Haiku 4.5 | $0.00005 | $0.00095 |
Grade A, and why
prp-mcp-create 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 6d 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:
- prp-mcp-create — 100% identical, 0 lines differ
- prp-mcp-create — 86% identical, 11 lines differ
- prp-mcp-create — 86% identical, 11 lines differ
- prp-mcp-create — 86% identical, 11 lines differ
- prp-mcp-create — 86% identical, 11 lines differ
- prp-mcp-create — 86% identical, 11 lines differ
- prp-mcp-create — 86% identical, 11 lines differ
- prp-mcp-create — 86% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create MCP Server PRP
Create a comprehensive Product Requirement Prompt (PRP) for building Model Context Protocol (MCP) servers with authentication, database integration, and Cloudflare Workers deployment.
Before you start ensure that you read these key files to get an understanding about the goal of the PRP: PRPs/README.md PRPs/templates/prp_mcp_base.md (This base PRP is already partially filled out based on the project structure but please finish it specific to the user's use case for an MCP server)
Users MCP use case: $ARGUMENTS
Purpose
Generate context-rich PRPs specifically designed for MCP server development, using the proven patterns in this codebase that is a scaffolding of a MCP server setup that the user can build upon, including GitHub OAuth, and production-ready Cloudflare Workers deployment.
None of the existing tools will likely be reused and the tools should be created for the users use case specifically tailored to their needs.
Execution Process
-
Research & Context Gathering
- Create clear todos and spawn subagents to search the codebase for similar features/patterns Think hard and plan your approach
- Gather relevant documentation about MCP tools, resources, and authentication flows
- Research existing tool patterns to understand how to build the users specified use case
- Study existing integration patterns in the codebase
-
Generate Comprehensive PRP
- Use the specialized
PRPs/templates/prp_mcp_base.mdtemplate as the foundation - Customize the template with specific server requirements and functionality
- Include all necessary context from the codebase patterns and ai_docs
- Add specific validation loops for MCP server development
- Include database integration patterns and security considerations
- Use the specialized
-
Enhance with AI docs
- The use might have added docs in PRPs/ai_docs/ directory that you should read
- If there are docs in the PRPs/ai_docs/ directory, review them and take them into context as you build the PRP
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.
- 6d ago First seen · 101 lines · 0 tokens per session scan A e0c579970885
prp-mcp-create is a command published in the GitHub repository coleam00/context-engineering-intro (13,820 stars, last pushed 5mo ago), licensed MIT. It adds 46 tokens to every session and 947 once invoked, about $0.0002 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.