Lesson 3 of 10 · Foundations
How MCP delivers context to agents
Short answer
MCP gives agents a standard way to ask a catalog, context platform or semantic layer for context. What matters is what the server returns, how it is scoped and how fresh it is.
By The Lineage Desk · Best Data Catalogs · Published
Desk research from public vendor pages. Editorial assessment.
3 min read
The Model Context Protocol is an open protocol that lets AI applications call tools and data sources through a standard interface. For data teams it solved a practical problem: instead of building a custom integration for every agent, a platform can run one MCP server that Claude, Cursor or an in-house agent can call.
Who ships an MCP server?
Most platforms in our ranking now do. Euno surfaces metadata-powered context to agents through MCP. Atlan serves context through SQL, APIs, an SDK and the Atlan MCP server. Collibra's MCP Server ships 25+ governance tools out of the box. DataHub lists MCP alongside an Agent Context Kit, Skills, APIs and SDKs. Cube includes an MCP server in its free plan. AtScale's MCP server exposes tools such as list_models and run_query. Select Star lists an MCP Server for Data. Alation's Ontologies can be published as an MCP server, in early access. We did not confirm one for Honeydew.
Source: Euno: Context for AI Agents
Source: Atlan homepage
Source: Collibra MCP Server
Source: DataHub Cloud context platform
Source: Cube pricing
Source: AtScale: semantic context for AI via MCP
Source: Select Star homepage
Source: Alation AIOS: Ontologies
What should the server return?
Having an MCP server is now table stakes. The useful question is what an agent gets back. A thin server returns table names and descriptions. A richer one returns lineage, certification status, sensitivity labels, usage and the business definition behind a field. AtScale returns certified metrics by name. Collibra exposes asset discovery, lineage and classification tools. Euno describes four layers in its agent context: semantic search and lineage insights, active metadata tags, usage insights and personas.
Source: Euno: Context for AI Agents
Why does scoping matter?
An agent serving a finance analyst should not see the same context as one serving a support lead. Scoping by role and task keeps answers relevant and limits what leaks into a prompt. Euno lists personas with role, task and access scope. AtScale gives each agent its own OAuth identity with row- and column-level security. Scoping also affects cost: Euno says a task-specific slice uses fewer tokens.
Source: Euno: Context for AI Agents
Source: AtScale: semantic context for AI via MCP
Source: Euno homepage
Is the context computed ahead of time or on demand?
Some designs gather context at query time by calling other tools. Others compute it ahead of time. Euno states it crawls metadata continuously and computes its context graph ahead of time rather than gathering context through MCP at query time. Precomputing favors speed and consistency; querying live favors freshness. Ask each vendor which it does and how quickly a change upstream shows up in what the agent receives.
What should you test in a trial?
Connect the agent you actually plan to use, ask ten questions your analysts answer every week, and read what the server returned for each, not only the final answer. Note which responses included lineage, which included trust labels, and which were plain descriptions. That record becomes the evidence for your shortlist.
Key points
- Most ranked platforms ship an MCP server; the difference is what it returns.
- Scope context by role and task to keep answers relevant and safe.
- Ask whether context is precomputed or gathered at query time, and how fresh it is.
Next lesson
4. Lineage from the warehouse to the agent