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feat(agents): mcpctl chat REPL + agent CRUD + completions (Stage 5)

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This is the moment the user can actually talk to an agent end-to-end:

  mcpctl create llm qwen3-thinking --type openai --model qwen3-thinking \
    --url http://litellm.nvidia-nim.svc.cluster.local:4000/v1 \
    --api-key-ref litellm-key/API_KEY
  mcpctl create agent reviewer --llm qwen3-thinking --project mcpctl-dev \
    --description "I review security design — ask me after each major change."
  mcpctl chat reviewer

Pieces:

* src/cli/src/commands/chat.ts (new) — REPL + one-shot. Streams the SSE
  endpoint and prints text deltas to stdout as they arrive; tool_call /
  tool_result events go to stderr in dim-style brackets so the chat
  output stays clean. LiteLLM-style flags (--temperature / --top-p /
  --top-k / --max-tokens / --seed / --stop / --allow-tool / --extra)
  layer over agent.defaultParams. In-REPL slash-commands: /set KEY VAL,
  /system <text>, /tools (list project's MCP servers), /clear (new
  thread), /save (PATCH agent.defaultParams = current overrides),
  /quit.

* src/cli/src/commands/create.ts — `create agent` mirroring the llm
  pattern. Every yaml-applyable field has a corresponding flag (memory
  rule); --default-temperature / --default-top-p / --default-top-k /
  --default-max-tokens / --default-seed / --default-stop /
  --default-extra / --default-params-file all populate agent.defaultParams.

* src/cli/src/commands/apply.ts — AgentSpecSchema accepts both `llm:
  qwen3-thinking` shorthand and `llm: { name: ... }` long form; runs
  after llms in the apply order so apiKey/llm references resolve. Round-
  trips with `get agent foo -o yaml | apply -f -` (memory rule).

* src/cli/src/commands/get.ts — agentColumns (NAME, LLM, PROJECT,
  DESCRIPTION, ID); RESOURCE_KIND mapping for yaml export.

* src/cli/src/commands/shared.ts — `agent`/`agents`/`thread`/`threads`
  added to RESOURCE_ALIASES.

* src/cli/src/index.ts — wires createChatCommand into the program; passes
  the resolved baseUrl + token so chat can stream SSE without going
  through ApiClient (which only does buffered request/response).

* completions/mcpctl.{fish,bash} regenerated. scripts/generate-completions.ts
  knows about agents (canonical + aliases) and emits a special-case
  `chat)` block that completes the first arg with `mcpctl get agents`
  names. tests/completions.test.ts: +9 new assertions covering agents in
  the resource list, chat in the commands list, --llm flag for create
  agent, agent-name completion for chat, etc.

CLI suite: 430/430 (was 421). Completions --check is clean.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Michal
2026-04-25 17:02:38 +01:00
parent 285be11dd5
commit 727e7d628c
10 changed files with 701 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
src/cli/src/index.ts
View File

@@ -18,6 +18,7 @@ import { createMcpCommand } from './commands/mcp.js';
import { createPatchCommand } from './commands/patch.js';
import { createConsoleCommand } from './commands/console/index.js';
import { createCacheCommand } from './commands/cache.js';
import { createChatCommand } from './commands/chat.js';
import { createMigrateCommand } from './commands/migrate.js';
import { createRotateCommand } from './commands/rotate.js';
import { ApiClient, ApiError } from './api-client.js';
@@ -216,6 +217,13 @@ export function createProgram(): Command {
log: (...args) => console.log(...args),
}));
program.addCommand(createChatCommand({
client,
baseUrl,
...(creds?.token !== undefined ? { token: creds.token } : {}),
log: (...args) => console.log(...args),
}));
program.addCommand(createPatchCommand({
client,
log: (...args) => console.log(...args),