Tools, APIs, and MCP
Agents call MCP servers, APIs, webhooks, and functions only when policy allows. Every call is written into the execution trail.
AI becomes useful when it can interact with the systems your business already runs on.
Who the action layer is for
Teams that need an agent to do something in an existing system: open a ticket, read an account, post an event. If that call is not granted, schema-checked, and recorded, you cannot tell a successful action from a hallucinated one.
How a tool call runs
The agent proposes an action. Obliq allows it only if that tool is granted and the arguments match the schema. The external system responds. The whole exchange is stored with the rest of the execution, next to the retrieval and the model call.
01AGENT decides an action is required
The model proposes a tool only after context is assembled.
02TOOL is selected under policy
Ungranted tools are not on the menu. Sensitive actions can wait for a person.
03API / MCP / WEBHOOK / FUNCTION is invoked
The call goes out with the arguments the schema allows.
04EXTERNAL SYSTEM returns a result
The payload comes back into the run, not into a side log you have to join later.
05RESULT is written into the execution trail
Latency, arguments, and the response are inspectable and replayable.
Connector types
Native client and server support. Agents discover and invoke MCP tools that policy grants, and each call lands on the execution ledger.
Authenticated endpoints for the systems the business already runs. Schema checks keep a tool call from sending a shape the API will reject.
Outbound and inbound events so an execution can wake a downstream workflow, or a workflow can start an execution.
Python and TypeScript compute for transformations that should happen inside the run, without handing the agent a raw shell.
Questions
Does Obliq support the Model Context Protocol?
Yes. Obliq provides native MCP client and server support. Agents discover and invoke tools through MCP under policy, and every invocation is stored on the execution trail.
How do AI agents call APIs safely?
Tools are granted per agent. Inputs are checked against registered schemas, and sensitive actions can require human confirmation before they fire. The call and its result are part of the trace.
What kinds of tools can an agent use?
MCP servers, REST and GraphQL APIs, event webhooks, and sandboxed Python or TypeScript functions. They share one execution record so you are not reconstructing the action from three vendor logs.