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  1. 1Getting Started
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  6. 6WorkflowsBeta
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  10. 10Developer Tools
Developer reference →Showcase →
Guides4. Agent actions & ToolsDeveloper tools
Chapter 4 · Agent actions & Tools

Developer tools

GitHub for issues / PRs / workflows / code search, and Vercel for deployments, logs, env vars, and domains.

7 min read

Developer tools let the agent bridge customer chat with the engineering stack — file issues from bug reports, look up PR / build status, fetch code to ground answers, trigger deploys, and read runtime logs when debugging.

GitHub

Tool id: github. Credential type: github. Auth: Personal Access Token.

26 actions across six groups: repo info, issues (CRUD + comments), PRs (CRUD + merge + comments), repository contents (get_file, list_directory, create_or_update_file, search_code), workflow runs (list / get / dispatch / cancel), releases / tags, and commits.

Setup — Personal Access Token

  1. Open github.com/settings/tokens.
  2. Decide between Fine-grained tokens (recommended — per-repo + per-permission scoping) and Classic tokens (broader).
  3. For a fine-grained token: select the repo(s), then grant the permissions you need — typical agent scopes are Contents: Read (or Read+Write), Issues: Read+Write, Pull requests: Read+Write, Workflows: Read+Write.
  4. Generate the token, copy it immediately (starts with github_pat_ for fine-grained or ghp_ for classic).
  5. Save as a GitHub credential. Set optional default_owner and default_repo so the LLM doesn't have to supply them every call.
Tip
Pair the capability profile with the right scopes:
  • Read-only profile + Contents: Read + Issues: Read = an answer-bot that grounds in code.
  • Read+write profile + Issues: Write + Pull requests: Write = the agent can triage and respond.
  • Full access profile + Workflows: Write = the agent can dispatch + cancel CI runs.

Use cases

  • Triage to engineering — agent calls create_issue with the chat transcript as the body and labels: ["bug", "triage"].
  • Status lookups — “is PR #1234 merged?” → get_pr. “Did last night's deploy succeed?” → list_workflow_runs filtered by workflow + branch.
  • Code Q&A — agent calls get_file or search_code before answering API-shape questions instead of guessing.
  • Trigger a deploy — dispatch_workflow on deploy.yml with inputs.environment = 'staging'.

Vercel

Tool id: vercel. Credential type: vercel. Auth: Access Token.

15 actions across projects, deployments (list / get / create / cancel / promote), build & runtime logs, env vars (list / create / update / delete), and domains.

Setup

  1. Open vercel.com/account/tokens.
  2. Click Create. Name it (e.g. chatzuri-agent), pick a scope: full account or a specific team. For team access, also note the team id (in the team URL or under Team Settings → General, ID starts with team_).
  3. Pick an expiration. Copy the token immediately — shown once.
  4. Save as a Vercel credential. If team-scoped, also paste the team_id. Optional default_project is the project name or id the agent uses when one isn't supplied.

Use cases

  • “Where's the preview?” → list_deployments filtered by branch and target. The dominant ask.
  • Build health — list_deployments target=production limit=1 + get_build_logs if it failed.
  • Runtime debugging — get_runtime_logs with since / until timestamps.
  • Promote to prod — promote_deployment on a verified preview.
Heads up
Vercel list_env_vars returns metadata only — never the decrypted values. That's deliberate: the LLM should never see your env secrets. If a customer asks “what's the value of DATABASE_URL?”, the right answer is to point them at the Vercel UI, not to print it.

Connecting the two

The killer combo is a single agent with both tools wired up: a support-engineering bridge that opens a GitHub issue with the transcript and a link to the latest Vercel preview URL all in one reply.

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Chatzuri

AI-powered agents are transforming customer interactions by providing instant, intelligent responses around the clock. They help businesses reduce operational costs, improve response times, and scale support without compromising quality. These agents understand natural language, learn from conversations, and integrate with existing systems to offer personalized experiences that enhance customer satisfaction and loyalty.

Chatzuri

AI-powered agents are transforming customer interactions by providing instant, intelligent responses around the clock. They help businesses reduce operational costs, improve response times, and scale support without compromising quality. These agents understand natural language, learn from conversations, and integrate with existing systems to offer personalized experiences that enhance customer satisfaction and loyalty.

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