Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@rollout-playbook-mcp generate a rollout plan for a cloud migration with low risk"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server that turns enterprise deployment best practices into resources and tools an AI assistant can use directly, instead of leaving them as static documents nobody re-reads once a project starts.
I spent years leading the teams that designed and deployed telco networks in the voice and wireless space, hundreds of production deployments for carriers worldwide. The hardest part was never the individual project. It was capturing what we learned each time and getting it into the hands of the next team fast enough to matter, so my team documented the patterns and built internal tools around them.
This is a small, deliberately scoped version of that same idea, rebuilt with AI in mind: instead of a static playbook doc that lives in a wiki nobody opens, the patterns are exposed as MCP resources an assistant can read, and as tools that generate a tailored plan on request. It's a toy compared to a real enterprise deployment practice, but the underlying instinct is the same one I've applied throughout my career and am now applying to Navi, an enterprise agentic AI platform I lead at IBM/HashiCorp: get expert knowledge out of a document and into the hands of whoever needs it, exactly when they need it.
Related MCP server: agentic-patterns
Resources — four playbooks, exposed as browsable documents:
Rollout Checklist (phase-by-phase deployment checklist)
Risk Assessment Framework (score a deployment's risk before committing to a plan)
Stakeholder RACI Template (who owns what, by phase)
Migration Cutover Plan (the specific moment traffic/data/users move to the new system)
Tools:
search_playbooks(query) — keyword search across all four playbooks, returns matching sections
generate_rollout_plan(project_type, team_size, risk_level) — generates a tailored rollout plan by combining the playbooks based on your inputs
Prompt:
assess_deployment_risk(project_description) — a pre-built prompt template that walks through the risk framework for a given project
git clone https://github.com/<your-username>/rollout-playbook-mcp.git
cd rollout-playbook-mcp
npm install
npm run build
npm startThe server communicates over stdio, so it's meant to be launched by an MCP client (Claude Desktop, Cursor, etc.), not run standalone in a terminal for interactive use.
Add to your client's MCP config (for example claude_desktop_config.json or a project's .mcp.json):
{
"mcpServers": {
"rollout-playbook": {
"command": "node",
"args": ["/absolute/path/to/rollout-playbook-mcp/dist/index.js"]
}
}
}Built on @modelcontextprotocol/sdk (TypeScript), using the high-level McpServer API
Playbook content is authored as markdown in /playbooks, then compiled into src/playbooks.ts as embedded string constants, so the built package is self-contained and works over npx without relying on relative file paths at runtime
Transport is stdio, the standard choice for a locally-run MCP server invoked by a desktop client
rollout-playbook-mcp/
├── playbooks/ # Source markdown for each playbook
├── src/
│ ├── playbooks.ts # Generated: embedded playbook content
│ └── index.ts # Server: resources, tools, prompt
├── package.json
├── tsconfig.json
└── README.mdMIT
Generate a tailored deployment rollout plan checklist based on project type, team size, and risk level
| Name | Required | Description | Default |
|---|---|---|---|
| team_size | Yes | Number of people on the deployment team | |
| risk_level | Yes | Overall risk level of the deployment | |
| project_type | Yes | Short description of the project, e.g. 'core banking platform migration' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only states that the tool 'generates a tailored checklist', but provides no information about mutability, authentication requirements, rate limits, or side effects. The lack of disclosure beyond purpose is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the core purpose. It is front-loaded with the action and resource, containing no redundant or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description should provide clues about the checklist format or structure. However, it only mentions the generation action without describing the return value or any behavioral aspects, making it incomplete for a tool that produces a plan.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with each parameter having a description. The description adds 'tailored' and reaffirms the three parameters, but does not provide additional semantic detail beyond what the schema already offers. A baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generate' and the specific resource 'deployment rollout plan checklist', and identifies the input factors (project type, team size, risk level). It distinguishes from the sibling tool 'search_playbooks', which searches existing playbooks rather than generating new plans.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a deployment rollout plan checklist is needed, but does not explicitly state when to use this tool versus the sibling 'search_playbooks'. No 'when not to use' or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search across all rollout/deployment playbooks for a keyword or phrase and return matching sections
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keyword or phrase to search for, e.g. 'rollback' or 'escalation' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It lacks details on scope (e.g., full-text search?), case sensitivity, or performance implications. The description only states the basic function without additional behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that conveys all essential information without unnecessary words. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description adequately states the purpose but lacks details about search behavior (e.g., partial matches, case sensitivity) that would enhance completeness for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with the single 'query' parameter already having a description and example. The tool description adds no new information beyond that. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'search', the resource 'rollout/deployment playbooks', and the result 'return matching sections'. It distinguishes from the sibling tool 'generate_rollout_plan' which is about creation, not search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for searching across all playbooks by keyword or phrase. It does not explicitly list when not to use or alternatives, but the sibling is clearly different, so context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0generate_rollout_plansearch_playbooksScored across 2 tools
The two tools have clearly distinct purposes: one searches existing playbooks, the other generates new plans. No overlap or confusion possible.
Both tools follow a consistent verb_noun pattern: search_playbooks and generate_rollout_plan. Naming is clear and predictable.
With only 2 tools, the server feels very thin for a 'rollout-playbook' domain. A typical set would include at least listing, retrieving, creating, and updating playbooks.
The domain of rollout playbooks is missing fundamental operations like create, read, update, delete, and execute playbooks. Only search and generate are present, leaving significant gaps.
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