# Model Context Protocol

The Model Context Protocol (MCP) is an open integration standard for secure access to external systems and contextual metadata by AI assistants and intelligent applications. MCP defines a standard framework for connecting large language models (LLMs) and AI-driven workflows with enterprise platforms.

The OvalEdge MCP Server exposes governed metadata, lineage information, glossary data, and platform knowledge through MCP-compatible interfaces. AI clients such as Claude Desktop, Cursor, Visual Studio Code extensions, etc., can retrieve metadata directly from OvalEdge while respecting existing access controls.

All requests are executed using the authenticated user’s permissions. Metadata visibility is governed by the access controls enforced in the OvalEdge platform.

## OvalEdge MCP Tools

The OvalEdge MCP Server provides read-only tools to search, explore, and understand governed metadata within the OvalEdge platform. These tools return structured responses that include governance context such as ownership, stewardship, classifications, and data quality indicators.

### Search & Discovery 

#### **Semantic Catalog Search (search\_catalog\_assets)**

Search cataloged assets using natural-language requests and hybrid search techniques that combine keyword relevance with semantic vector matching. Results include governance metadata and contextual asset information to help AI clients efficiently identify relevant data assets. 

#### **Asset Metadata Retrieval (catalog\_asset\_details)**

Retrieves detailed metadata for a specific catalog object, including object properties, schema details, ownership information, classifications, and governance attributes. Assets can be retrieved using either a fully qualified name or an internal object identifier.

#### **Glossary Term Lookup (lookup\_glossary\_term)**

Accesses the governed business glossary terms and definitions maintained in OvalEdge. The tool connects business terminology with associated physical data assets, related concepts, and governance context for AI client processing.

#### **Tag Discovery (lookup\_tags)**

Retrieves classification tags and metadata labels associated with governed assets. This capability provides AI systems with sensitivity classifications, governance tagging structures, and organizational metadata standards.

#### **Lineage Traversal (asset\_lineage)**

Explores upstream and downstream lineage relationships for cataloged assets. The OvalEdge MCP Server enables AI clients to trace dependencies across datasets, files, and related systems for data movement and impact analysis workflows. 

#### **Entity Relationship Discovery (table\_entity\_relationships)**

Retrieves structural relationships between database entities, including primary-key and foreign-key mappings. This capability provides relationship information for AI applications to identify dataset connectivity and generate join recommendations for analytical workflows.

#### **Column Profiling Information (column\_profile\_statistics)**

Accesses profiling statistics for supported table and file assets. Profiling information includes column-level characteristics for governance-aware metadata analysis.

#### **Documentation Search (search\_platform\_docs)**

The OvalEdge MCP Server includes documentation search capabilities that help users find relevant procedures, how-to guides, and usage information for the OvalEdge application through natural-language queries. 

## Authentication

The OvalEdge MCP Server uses API Key-based authentication. Generate credentials from within the OvalEdge application and paste them into the MCP configuration file. All MCP requests will run within the security scope of the account that generated the token. 

Note: Keep your User Secret private. If it's ever compromised, regenerate it from the OvalEdge application. 

## REST APIs Available

The OvalEdge MCP Server consumes REST APIs for metadata discovery, lineage analysis, glossary retrieval, documentation search, and governance exploration workflows. 

### Authentication API

<table><thead><tr><th width="106.3333740234375">Method</th><th width="224.6666259765625">Endpoint</th><th>Description</th></tr></thead><tbody><tr><td>POST </td><td>/user/token/generate </td><td>Generates a JWT session token for MCP API authentication. </td></tr></tbody></table>

### Metadata & Discovery APIs&#x20;

<table><thead><tr><th width="105">Method</th><th width="223.6666259765625">Endpoint</th><th>Description</th></tr></thead><tbody><tr><td>GET </td><td>/v1/mcp/search-catalog </td><td>Performs a hybrid metadata search across catalog assets. </td></tr><tr><td>GET </td><td>/v1/mcp/object-details </td><td>Retrieves detailed metadata for a catalog object. </td></tr><tr><td>GET </td><td>/v1/mcp/data-sources </td><td>Returns accessible data source connections. </td></tr><tr><td>GET </td><td>/v1/mcp/column-profile </td><td>Retrieves profiling statistics for supported assets. </td></tr><tr><td>GET </td><td>/v1/mcp/glossary-terms </td><td>Retrieves glossary term metadata and definitions. </td></tr><tr><td>GET </td><td>/v1/mcp/tags </td><td>Retrieves governance tag details and classifications. </td></tr></tbody></table>

### Lineage & Relationship APIs&#x20;

<table><thead><tr><th width="105">Method</th><th width="225">Endpoint</th><th>Description</th></tr></thead><tbody><tr><td>GET </td><td>/v1/mcp/entity-relationships </td><td>Returns PK/FK relationship mappings between entities. </td></tr><tr><td>GET </td><td>/v1/mcp/lineage </td><td>Retrieves lineage graph information for assets. </td></tr></tbody></table>

### Documentation Intelligence API&#x20;

<table><thead><tr><th width="105">Method</th><th width="225">Endpoint</th><th>Description</th></tr></thead><tbody><tr><td>GET </td><td>/v1/mcp/search-platform-docs  </td><td>Performs semantic search across OvalEdge platform documentation. </td></tr></tbody></table>

## AI and Embedding Integration

OvalEdge supports semantic search and vector-based retrieval workflows through integrations with leading AI and embedding providers, including OpenAI, Google Gemini, and Amazon Bedrock.

The platform allows organizations to configure and select embedding models based on their specific requirements and retrieval use cases.

## Open Source MCP Client

The OvalEdge MCP server implementation is available as open source on GitHub, where users can find setup instructions, configuration details, and usage examples.\
<https://github.com/ovaledge/oe_mcp>

***

Copyright © 2026, OvalEdge LLC, Peachtree Corners, GA, USA.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.ovaledge.com/release8.1/ovaledge-mcp-server-new/model-context-protocol.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.