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., "@ord-mcp-server explain the ORD Product concept"
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.
A Model Context Protocol (MCP) server providing access to ORD (Open Resource Discovery) specification and concept explanations.
npm installAdd this to your MCP client configuration:
{
"mcpServers": {
"ord-mcp-server": {
"command": "node",
"args": ["path/to/ord-mcp-server/src/ord-mcp-server.js"],
"env": {}
}
}
}get_ord_specificationRetrieves the latest ORD specification document from GitHub.
{
"name": "get_ord_specification",
"arguments": {}
}explain_ord_conceptProvides detailed explanation of ORD concepts with examples.
{
"name": "explain_ord_concept",
"arguments": {
"concept": "Product"
}
}Supported concepts: Product, Package, ConsumptionBundle, APIResource, EventResource
ord://specification/latestDirect access to the latest ORD specification document as a Markdown resource.
Related MCP server: SNOMED CT MCP Server
This project follows unified assistant + engineering standards defined in ENGINEERING_PROFILE.md. All assistants must follow these standards for generation, refactor, review, debugging, and optimization tasks.
MIT License
Explain ORD concepts with examples
| Name | Required | Description | Default |
|---|---|---|---|
| concept | Yes | ORD concept to explain |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action ('explain with examples') and adds no context about output format, whether examples are generated dynamically, or any limitations. The description essentially restates the tool name and does not deepen the agent's understanding of what to expect.
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, front-loaded sentence with no filler. Every word contributes to the purpose, making it highly concise and well-structured.
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's low complexity (one parameter, no output schema), the description is functional but leaves out contextual details such as the expected form of the explanation, relationship to `get_ord_specification`, and any usage examples. It is adequate but not rich enough to be fully self-sufficient.
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 input schema has 100% parameter description coverage, including an enum with a clear description of `concept`. The tool description adds no additional parameter semantics beyond what the schema already provides, so the 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 uses a specific verb ('explain') and identifies the resource ('ORD concepts') with a clear added value ('with examples'). It clearly distinguishes from the sibling tool `get_ord_specification`, which implies retrieving the specification rather than explaining concepts.
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 the tool is for gaining understanding of ORD concepts, but it gives no explicit guidance on when to use it versus `get_ord_specification`, nor any exclusions or prerequisites. The usage context is inferable but not directly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get and use the latest ORD specification document
| 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 of behavioral disclosure. The phrase 'Get and use' is ambiguous and does not explain what happens after retrieval (e.g., return format, side effects, or whether the document is stored in context).
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 one concise sentence, front-loaded with the action. However, it is too brief to cover essential details, so it is not a perfect 5.
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?
There is no output schema and no annotations, so the description must explain return values or behavior. It does not, leaving the agent uncertain about what the tool actually returns or how to use the retrieved specification.
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, and the schema is empty, so description compensation is not required. Per baseline for 0 parameters, a score of 4 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 states a clear verb+resource ('Get and use the latest ORD specification document'), which distinguishes it from the sibling tool 'explain_ord_concept' that explains concepts. However, 'use' is vague and not fully specific about what the tool does with the document.
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?
There is no guidance on when to use this tool versus 'explain_ord_concept'. The description does not mention any context, prerequisites, or exclusions, leaving the agent without direction on selecting between the two tools.
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.
v0.0.1explain_ord_conceptget_ord_specificationScored across 2 tools
The two tools are clearly distinct: one retrieves the specification document, the other explains concepts. There is no overlap in their purposes.
Both tool names follow a consistent verb_noun pattern: get_ord_specification and explain_ord_concept. The naming is clean and predictable.
With only 2 tools, the server feels thin for a general-purpose ORD resource, though it may be sufficient for a narrowly focused use case. It sits at the borderline end.
The server provides access to the spec and concept explanations, but lacks a way to list available concepts or navigate different versions, leaving minor gaps in discoverability.
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