free-blueprints
Server Details
Free AI agent blueprints for procurement and onboarding. No signup, no API key.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- agent-blueprint/free-blueprints
- GitHub Stars
- 0
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Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.3/5 across 2 of 2 tools scored.
The two tools are clearly distinct: one lists available blueprints, the other retrieves a specific blueprint by ID. There is no overlap in purpose.
Both tool names follow a consistent verb_noun pattern (list_blueprints, get_blueprint), making the API predictable and easy to understand.
With only two tools, the server is slightly under the typical well-scoped range, but the scope (listing and retrieving free blueprints) is narrow enough that two tools feel reasonable.
For a catalog of static blueprints, listing and downloading are the essential operations. There are no obvious missing actions like update or delete, as blueprints are read-only resources.
Available Tools
2 toolsget_blueprintAInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns a JSON manifest (not a direct file download) and specifies the contents (SKILL.md, reference docs, implementation guide). It also notes the format matches the paid export, which is useful context. It doesn't mention auth or side effects, but for a read-style download tool this is reasonably transparent.
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 sentences, front-loaded with the action and resource, followed by necessary details about the return format and comparison to the paid export. Every word earns its place, no filler.
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?
With a single parameter, no output schema, and a clear sibling, the description fully covers what the tool does and what it returns. The explanation of the JSON manifest contents and the note about the paid export provide sufficient context for an agent to use the tool correctly.
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%, and the parameter description already states 'Blueprint ID from list_blueprints'. The tool description adds only 'by ID', which does not meaningfully enhance the semantic understanding beyond what the schema provides. Baseline 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 uses a specific verb ('Download') with a clear resource ('free AI agent blueprint by ID'), and explicitly distinguishes itself from the sibling tool list_blueprints by focusing on retrieval rather than listing. The mention of the JSON manifest further clarifies what the tool actually does.
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 clearly implies the tool is used after list_blueprints to obtain a specific blueprint, and the parameter description references 'Blueprint ID from list_blueprints'. However, it does not explicitly state when not to use it or name an alternative, which keeps it from a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_blueprintsAInspect
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 | |||
Tool Definition Quality
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 discloses that the tool returns a catalog, that the blueprints are free, and that no authentication is required. This is above-average transparency for a simple read-only list, though it omits details like pagination or sort order.
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 only two sentences, front-loaded with the core action and followed by a valuable access note. There is no filler, redundancy, or wasted wording.
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 zero-parameter listing tool, the description is quite complete: it specifies what is listed, the condition (free), the return type (catalog), and access requirements. It could have mentioned that get_blueprint retrieves specifics, but the sibling tool's existence implicitly covers that. Minor gap in not describing catalog fields, but an agent can easily discover them by invoking the tool.
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 the input schema is trivially complete and the baseline for 0-param tools is 4. The description does not need to explain parameters, and it adds no unnecessary parameter information.
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 uses the specific verb 'List' and names the resource 'available free AI agent blueprints', which clearly states the function and distinguishes it from the sibling tool 'get_blueprint'. It is unambiguous and immediately understandable.
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: you use this tool to see what free blueprints are available, and the note about no signup or API key suggests low-friction access. However, it does not explicitly contrast with the sibling tool 'get_blueprint' or state when not to use this tool, so 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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
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For server owners:
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Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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