What is an agent harness?

An agent harness is the software layer that runs an agent session. It prepares the instructions, context and tool definitions for the language model, applies approval rules, routes tool calls, and records what happened. OrcA 0.4 is a harness specialised for MCP and API work inside VS Code; it does not edit files or run a terminal.

What is progressive tool discovery in MCP?

Instead of sending every tool definition on every turn, the client sends a small search_tools tool plus a list of tool names, and loads full definitions only when they are needed. OrcA 0.4 searches with each message you send, loads matching tools before the model answers, appends them so prompt caches keep working, and resets them on New Chat.

How do I reduce MCP context bloat?

Serve your API with a search tool (for HAPI, --deferred-tool-search or --capability-graph) and keep orca.chat.toolDiscovery on auto, or set it to always to let OrcA search any server locally. The Harness tab shows the tokens your tools cost upfront compared with the full catalog.

Can OrcA connect to MCP servers not built with HAPI?

Yes. Use Add External MCP Server… with a name, a Streamable HTTP URL and optional headers, or Import MCP Servers… from .vscode/mcp.json and your user mcp.json. External servers get chat, traffic, proxy capture and Add to VS Code. stdio and SSE servers are not supported.

Can I get an OpenAPI contract for a hand-written MCP server?

Yes. Generate Contract infers a deterministic OpenAPI 3.1 document from a connected server's tools, resources, resource templates and prompts. It is linked to the server, so Open Operation in Contract works, and OrcA marks the contract out of date when the server's tools change.

Does OrcA 0.4 support OAuth-protected MCP servers?

Not yet. OrcA connects with static headers, such as a bearer token or an API key, stored in VS Code's secret storage. OAuth support is planned.

What is an MCP-based agent?

An MCP-based agent is a Large Language Model that runs in a loop inside a harness and acts through Model Context Protocol (MCP) tools. In the HAPI MCP stack, the tools come from your OpenAPI and Arazzo contracts, the harness is OrcA or your agent platform, and a role narrows the agent to a job.

Do I need to write code to give an agent tools from my API?

No. HAPI reads an OpenAPI 3.x or Arazzo 1.1 contract and serves each operation or workflow as an MCP tool. With --headless --url, HAPI forwards each tool call to your existing backend, so the contract is the whole integration.

What is an agent harness, and what does OrcA do?

A harness prepares the instructions, context, and tool definitions for the model, applies approvals, routes tool calls, and records what happened. OrcA is a VS Code harness for MCP and API work: it connects models to MCP servers, loads tools progressively, asks before tools that are not read-only, and shows every exchange in its Traffic view. It does not edit files or run a terminal.

How do I stop an agent from loading too many tools?

Serve the contract with --capability-graph or --deferred-tool-search. HAPI defers operation tools behind a tool_search tool, and OrcA loads only the tools each message needs, while listing the names of the rest so the model knows they exist.

How do I test an MCP agent before production?

Test plans offline with hapi graph audit and hapi graph simulate, then test conversations in OrcA. The Context tab shows goals, required facts, and gates, and the Traffic view shows each model round and tool call with latency, tokens, and cost.

How do I deploy an MCP server built from an OpenAPI contract?

Run hapi deploy with an HTTPS contract URL and your backend URL to deploy a headless MCP server to Cloudflare Workers, or select Deploy MCP Server on the contract in OrcA.

What is an intent-based API?

An intent-based API contract adds a decision layer to an API description: when each operation applies, what it changes, which verified facts it requires and produces, and what a failure means. HAPI compiles this metadata into a capability graph, so an agent can map a request to a goal and receive a deterministic plan that names the missing information.

Are Arazzo workflows and HAPI intent-based contracts redundant?

No. Arazzo workflows encode fixed sequences that must always run together. Intent-based contracts let a planner decide which atomic operations, and which workflows, to run from the current state. A workflow with x-hapi-planning becomes one atomic step in those plans.

Are Arazzo workflows all-or-nothing?

No. An Arazzo workflow stops at the first step whose success criteria fail, but earlier steps have already changed state in the source API. Neither Arazzo nor HAPI undoes earlier steps automatically, so compensation is a design decision.

Are API workflows idempotent?

Only when every step is idempotent or carries an idempotency key. A step that creates a record with a new identifier is not idempotent, so running the workflow twice creates two records.

Is an OpenAPI description enough for AI agents?

It is enough to call an API, not to use it safely. OpenAPI describes paths, schemas, status codes, and security, but not when an operation is the right choice, what it changes, what must already be true, or what a failure means for the next step.

Does intent-based planning stop LLM hallucinations?

It limits their impact. The model still interprets the request, but permissions, prerequisites, ordering, and confirmations come from the contract and are enforced deterministically. The planner never invents facts; it accepts only facts the client verified and reports the missing ones.

How do I debug an MCP server in VS Code?

Install OrcA 0.3, right-click a running server in the MCP Servers view and choose Connect, then run OrcA: Show Traffic. Every JSON-RPC request, response and notification appears in a live timeline with latency, size and status. You can open any payload as JSON, diff two calls, copy a call as cURL, and replay it.

How can I see what GitHub Copilot sends to my MCP server?

In OrcA, right-click the server and choose Expose via Proxy, then Add to VS Code and pick Through OrcA (captured). OrcA registers a local proxy address (http://127.0.0.1:7331/mcp/ ) in .vscode/mcp.json. Copilot keeps working as usual, and every request it sends shows up in OrcA Traffic with the client name.

Can I chat with my MCP server without an API key?

Yes. OrcA's Chat can use the language models VS Code makes available to extensions, such as GitHub Copilot's models, which need no extra key. OpenAI, Anthropic, Groq, OpenRouter, Ollama, LM Studio and any OpenAI-compatible endpoint are also supported; their keys are stored in VS Code's secret storage.

Is OrcA an alternative to MCP Inspector?

For day-to-day work inside VS Code, yes. OrcA connects to several MCP servers at once, lists their tools, resources and prompts, lets an LLM use them in a chat, and records both the LLM rounds and the MCP tool calls in one correlated graph. It also captures traffic from other MCP clients through a local proxy. Calling a single tool by hand from a form is planned for a later release.

Does OrcA send my traffic or prompts anywhere?

No. OrcA has no telemetry and no vendor SDKs. Traffic stays in memory with secrets redacted, the chat only contacts the LLM provider you selected, the proxy listens on 127.0.0.1 only, and nothing is written to disk unless you save a session file.

Does OrcA support OAuth-protected MCP servers?

Not yet. OrcA 0.3 connects with static headers such as a bearer token (Set Headers…). OAuth-protected servers are detected and reported; OAuth support is planned for an upcoming release.

OrcA (Orchestrator for APIs and Agents) is a VS Code extension, part of the HAPI MCP Stack. It turns OpenAPI and Arazzo contracts into MCP servers inside the editor: it finds your contracts, previews their MCP tools, runs them locally with the HAPI CLI, deploys them, and registers them with your AI assistant.

How do I turn an OpenAPI or Swagger spec into an MCP server in VS Code?

Install OrcA, open your OpenAPI 3.x file, and click Run with HAPI in the editor title bar. OrcA starts hapi serve on a free port and shows the server in the MCP Servers view; Add to VS Code then registers it in .vscode/mcp.json. Swagger 2.0 documents need converting to OpenAPI 3 first.

Can I see which MCP tools an API will expose before running it?

Yes. OrcA's Dry Run runs hapi serve --dry-run and opens the tool list beside your contract as JSON (a ChatGPT App submission dump), Markdown (an AI-agent system prompt template), YAML, or a table. No server is started.

What happens if port 3000 is already in use?

OrcA checks that the port is really free before starting HAPI. If port 3000 is taken, it starts on the next free port and notes it; if you chose the port yourself, it asks and offers the next free one. It never gives two of its own runs the same port.

Does OrcA replace the runMCP VS Code extension?

Yes. The runMCP VS Code extension is deprecated and all of its features, including deploying and managing MCP servers, now live in OrcA.

What is a deterministic workflow for an AI agent?

A deterministic workflow is an explicit sequence of approved operations, prerequisites, and failure paths. An AI agent may interpret a user's request, but the workflow defines how the resulting business action executes and how the outcome is verified.

What is the difference between AI planning and deterministic execution?

AI planning interprets ambiguous language, proposes an outcome, and asks for missing details or approval. Deterministic execution carries out the approved operations in a declared order, applies API validation and authorization, and handles known failures or compensation paths.

How do HAPI Workflows expose deterministic workflows through MCP?

HAPI Workflows validates an Arazzo workflow that references documented OpenAPI operations, then exposes each supported workflow as an MCP tool. An agent can request a meaningful outcome instead of manually coordinating every low-level API call.

Does HAPI Workflows replace Camunda or Temporal?

Not necessarily. Camunda, Temporal, and other workflow engines remain appropriate when they own durable process state, timers, people tasks, or complex orchestration. HAPI Workflows provides an API-contract and MCP layer for declared OpenAPI workflows; it should complement, not duplicate, an existing system of record.

Why expose a workflow as an MCP tool instead of every API endpoint?

A workflow tool gives the agent a meaningful outcome with declared step dependencies, rather than asking it to reconstruct a business process from unrelated low-level operations. The underlying APIs remain the authority for authentication, authorization, validation, and business rules.

What is MCP elicitation?

MCP elicitation is a standardized feature (elicitation/create) that lets a server pause mid-task and ask the connected user for structured, schema-validated information through the client, instead of guessing a missing value or failing the request outright.

Is MCP elicitation a bad practice?

No, but it's easy to overuse. Elicitation is the right tool for confirming a destructive action or asking for something only the user can know. It's the wrong tool for filling in a required field your API already knows is required, or for chaining together a multi-step process — both of those are design problems elicitation just papers over.

How do I reduce the need for elicitation in my MCP tools?

Design each API operation to be atomic and explicit about its required parameters in the OpenAPI spec, so an agent can see what's required before it calls the tool instead of finding out mid-call. For processes that genuinely need multiple steps, define the sequence explicitly with HAPI Workflows (Arazzo) instead of letting the model improvise it through back-and-forth elicitation.

What's the difference between elicitation and a multi-step workflow?

Elicitation asks a human for one flat, primitive value mid-call, at runtime, on demand. A workflow defines the dependency between steps — 'this step's input comes from that step's output' — as an explicit, deterministic sequence the server executes, with no back-and-forth with the user needed to figure out what comes next.

Can HAPI Workflows replace MCP elicitation for multi-step processes?

For the class of elicitation that exists only to chain calls together — 'what's the ID from the last step?' — yes. HAPI Workflows, built on the Arazzo specification, let you define that dependency once as data, so the sequence runs deterministically instead of relying on the model to ask and remember it correctly every time.

What data can I request through MCP elicitation?

The MCP spec restricts elicitation's requestedSchema to flat objects with primitive properties only — strings, numbers, booleans, and enums, no nested objects or arrays of objects. It also explicitly forbids requesting sensitive information through elicitation.

8 publicaciones etiquetados con "Arazzo"

Arazzo defines API workflows—sequences of calls and dependencies—to deliver outcomes using API specs like OpenAPI.

Ver Todas las Etiquetas

OrcA 0.4: An MCP Agent Harness in VS Code, for Any MCP Server

OrcA 0.4 turns VS Code into an agent harness for MCP and API work. Connect any Streamable HTTP MCP server, not only HAPI ones. Let the agent load tools as the conversation moves instead of drowning in 40 schemas per turn. Read and edit exactly what the model is told, and see the goals, facts and plans behind a multi-step request. Still free, still no telemetry (by default).

How to Build and Manage MCP-Based Agents from Your API Contracts

What you will learn

How to build an MCP-based agent without writing tool code.
How to define capabilities in OpenAPI 3.x contracts with intent-based planning.
How to give the agent a transparent harness in VS Code.
How to test, measure, deploy, and manage an MCP-based agent over time.

How to Make Intent-Based Contracts and APIs AI-Ready

What you will learn

How intent-based planning balances model interpretation with deterministic enforcement.
How to make an OpenAPI contract intent-based AI contract.
How to verify contracts and workflows.
How to observe re-planning behavior in VS Code.

OrcA 0.3: See Every MCP Tool Call and LLM Round in VS Code

OrcA 0.3 lets you see exactly what your AI agent does with your MCP servers, without leaving VS Code. Chat with your servers using the model you already have, watch every LLM round and every MCP tool call in a live Git-style graph, and capture what GitHub Copilot or Claude send to your server. No telemetry, no extra app, no code changes.

OrcA for VS Code: From OpenAPI and Arazzo to MCP Server, Without Leaving Your Editor

Your API contracts live in your editor. Until today, your MCP servers lived somewhere else: a terminal running a CLI, a browser tab with a console, a dashboard for deployments, and a doc page you kept re-reading. OrcA MCP puts that whole journey in one place.

From API to MCP with Deterministic Workflows

An AI agent can understand what a person means. It should not improvise how a production business process runs.

The architecture is simple:

AI interprets intent. APIs define the rules. Deterministic workflows execute approved outcomes.

MCP Elicitation Is a Symptom, Not a Strategy

If your tool server needs to interrupt the model to ask a question, ask why the question wasn't answerable before the call started.

MCP elicitation is a real, spec-defined capability, and it solves a real problem. It's also becoming a convenient place to hide a design problem instead of fixing it.

When Not to Use AI: Deterministic Workflows in Production Systems

AI is everywhere. Too everywhere. From answering "hi" to deciding how your production platform runs sanity checks, we've quietly crossed a line. Not because AI can't do these things, but because it shouldn't do all of them.