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., "@free-blueprints List available blueprints and deploy the RFx procurement one."
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.
Pre-built AI agent blueprints for common business processes. No signup, no API key, no token.
Any MCP-compatible coding agent can discover and consume these blueprints immediately.
Blueprint | Agents | Pattern | Description |
RFx Procurement Automation | 5 | Manager-Workers | Automate RFx intake, requirements analysis, response composition, and compliance validation |
Employee Onboarding | 5 | Manager-Workers | Automate document processing, IT provisioning, training assembly, and progress tracking |
Related MCP server: swiftsign-mcp
npm install -g @agent-blueprint/free-blueprintsAdd to your project's .mcp.json:
{
"mcpServers": {
"free-blueprints": {
"command": "free-blueprints-mcp"
}
}
}Then ask your agent: "List available blueprints and deploy the RFx procurement one."
For Managed Agents, AI Control Tower, or any HTTP MCP client:
https://free-blueprints-mcp.agentblueprint.workers.dev/mcplist_blueprintsReturns the catalog of available free blueprints.
get_blueprintReturns a full blueprint by ID as a JSON manifest with all files ready to write to disk.
Parameters:
blueprintId (string, required) — Blueprint ID from list_blueprints
Each blueprint includes:
SKILL.md — Overview with frontmatter metadata
GETTING-STARTED.md — Step-by-step implementation guide
AGENTS.md — Universal agent sync rules
implementation-state.yaml — Progress tracking template
Reference docs — Agent specs, architecture decisions, guardrails, evaluation criteria, platform connectivity, business context
These free blueprints cover generic business processes. For blueprints tailored to your company (your systems, your data, your org structure, your financials), visit agentblueprint.ai.
MIT
Download a free AI agent blueprint by ID. Returns a JSON manifest with all files (SKILL.md, reference docs, implementation guide) ready to write to disk. Same format as the paid Agent Blueprint export.
| Name | Required | Description | Default |
|---|---|---|---|
| blueprintId | Yes | Blueprint ID from list_blueprints |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It does disclose the return format clearly ('JSON manifest with all files... ready to write to disk') and notes format parity with the paid export. It does not cover error behavior, auth requirements, or invalid-ID handling, but for a download operation the return format disclosure is substantive and actionable.
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?
Two efficient sentences with no filler. The first sentence fronts the core purpose (verb + resource + key), and the second adds the return format. It earns its place without redundancy, though it could arguably compress the file enumeration.
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?
For a simple one-parameter download tool with no output schema and no annotations, the description is fairly complete: it states the return format, the file contents, and the free-vs-paid distinction. Notable gaps are error behavior and authentication prerequisites, but these are minor for a read-style download of this simplicity.
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?
The schema covers the single parameter blueprintId at 100% and even gives provenance ('from list_blueprints'). The description adds no further format or syntax detail beyond the schema, so the baseline of 3 applies. The description's value here is limited since the schema already fully documents the parameter.
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?
States a specific verb ('Download'), a concrete resource ('AI agent blueprint'), and the identifying key ('by ID'). The description also scopes it to 'free' blueprints and contrasts it with the paid export, which clearly differentiates it from the sibling list_blueprints and from the paid export path. An agent can tell exactly what this tool does and how it differs without opening the schema.
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 gives context — it is the free variant, and the parameter description ('Blueprint ID from list_blueprints') implies a prerequisite workflow. However, the tool description itself does not explicitly state when to use it versus the sibling tool or the paid export, nor does it state when NOT to use it. The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available free AI agent blueprints. Returns catalog of pre-built blueprints for common business processes. No signup or API key required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that no signup or API key is required, a useful behavioral trait, and states that it returns a catalog. However, it does not explicitly confirm it is a read-only operation, nor mention pagination, rate limits, or expected response format. Given the simplicity of the tool, this is adequate but not exhaustive.
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 two sentences with zero redundancy. It leads with the core action and then adds a crucial access detail. Every word earns its place, making it highly efficient and easy to parse.
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?
For a parameterless, output-schema-less list tool, the description states what it does and returns. It does not specify the exact structure of the catalog, but given the simplicity and that no output schema exists, the description is sufficiently complete for an agent to correctly invoke it and understand the general result. The lack of sibling differentiation is a minor gap.
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?
The tool has zero parameters, so schema coverage is trivially high. The description correctly adds no parameter information because none are needed. With 0 params, the baseline is 4, and the description appropriately does not discuss parameters.
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 states a specific verb ('List') and a clear resource ('available free AI agent blueprints'). It clearly identifies the tool's function as returning a catalog, which distinguishes it from a retrieval tool like get_blueprint, though it does not explicitly name the sibling. The purpose is clear and unambiguous.
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 context by noting that no signup or API key is required, which hints at accessibility but does not explicitly compare to alternatives. It does not state when to use this tool versus get_blueprint, nor provide any exclusion criteria. The guidance is minimal but not misleading.
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.
v1.0.1get_blueprintlist_blueprintsScored across 2 tools
The two tools have clearly distinct purposes: listing available blueprints versus downloading a specific blueprint by ID. There is no overlap or ambiguity, as one provides an overview and the other retrieves a detailed artifact.
Both tool names follow a consistent verb_noun pattern: 'list_blueprints' and 'get_blueprint'. This makes the API predictable and easy to reason about.
With only two tools, the surface is minimal but aligns with a read-only catalog service. It borders on thin, but the scope is narrow—listing and retrieving blueprints—so the count is acceptable though not rich.
The lifecycle for a catalog is complete: list and get cover discovery and retrieval. There's no need for update/delete since blueprints are static free resources. Minor gaps like search or filter are absent, but the core workflow is fully supported.
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