
parallel-mcp-setup
Use when installing, configuring, or troubleshooting an authenticated Parallel Search MCP connection in Bifrost, including gateway-level search settings and downstream tool access.
- Installs
- 1.1K
- Last 24h
- +26
- Trending rank
- #4797
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/parallel-web/parallel-agent-skills --skill parallel-mcp-setup- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads parallel-mcp-setup automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Set up Parallel MCP
Configure and verify authenticated Parallel MCP connections. The first supported setup path is Parallel Search MCP through Bifrost; the steps below apply to that path. Tool descriptions supplied by the MCP server govern search and fetch usage; this skill covers installation and gateway configuration.
1. Inspect the deployment
Identify the target Bifrost instance, its configuration source (dashboard or config.json), and the intended downstream agent or virtual key. Inspect existing MCP clients before changing anything; update the matching Parallel connection instead of adding duplicates. Preserve unrelated clients, access rules, and tool approval settings.
Confirm access to Bifrost management and a Parallel account credential. These are separate from the downstream agent's Bifrost credential. If access or a secret is missing, prepare the configuration and report what is needed without claiming installation succeeded. Keep secrets in the deployment's secret manager or environment; never put them in skill files, source control, prompts, or tool arguments.
2. Configure the authenticated connection
Use https://search.parallel.ai/mcp-oauth, which requires authentication. Choose Headers auth for a shared Parallel API key, or Bifrost's OAuth flow when that is the deployment's chosen authentication method. Do not change an authentication failure into an unauthenticated connection.
For shared-key configuration, provision PARALLEL_MCP_AUTHORIZATION in the Bifrost server environment with the complete value Bearer <Parallel API key>. The env. reference substitutes the whole header value; it does not prepend Bearer.
In MCP Gateway → New MCP Server, choose HTTP, the URL above, and Headers auth. Set Authorization to the environment-variable reference using the UI's env-var picker. Allow web_search and web_fetch for this connection.
For a file-managed deployment, merge this client entry into the existing mcp.client_configs array:
{
"name": "parallel-search",
"connection_type": "http",
"connection_string": "https://search.parallel.ai/mcp-oauth",
"auth_type": "headers",
"headers": {
"Authorization": "env.PARALLEL_MCP_AUTHORIZATION"
},
"is_ping_available": false,
"tools_to_execute": ["web_search", "web_fetch"]
}
For OAuth, use the same endpoint with Bifrost's OAuth auth type and complete its admin verification/sign-in flow. Use per-user authentication only when each caller should use their own Parallel account. See Bifrost authentication for the chosen flow.
Respect the deployment's reload procedure. Existing connection URL/auth-type changes may require replacing the client because Bifrost treats those fields as immutable; plan that replacement without deleting a working client first. File-managed entries must be updated in the file as well, or a restart can recreate the old configuration.
3. Configure gateway controls and access
Keep Parallel’s defaults by omitting search-setting headers and URL parameters. When the user explicitly requests a particular search mode, source policy, or response budget, read gateway search controls. Apply these as upstream connection settings. Keep the chosen credential and search policy under gateway administration; do not enable caller-supplied overrides of these headers unless explicitly intended.
Confirm Bifrost discovers both tools and grants the intended downstream client or virtual key access to them. Connect the agent to the deployment's Bifrost MCP gateway with its configured downstream authentication. Use the actual discovered tool names, which may be prefixed. Installing this skill does not itself register or expose the MCP tools.
4. Verify through Bifrost
- Confirm the upstream connection is healthy and authenticated, with
web_searchandweb_fetchdiscovered. - Confirm the intended downstream agent can discover both tools through Bifrost.
- Run a small search for official Python asyncio documentation, then fetch a returned documentation URL. Use the tools' current schemas and descriptions. Confirm successful results and inspect per-URL errors.
- Inspect Bifrost's tool-call records to confirm the intended connection was used. Verify account attribution and any configured mode or source policy in available upstream request metadata or account logs; result quality alone does not prove the mode used.
- Report exactly what was verified and what remains untested. A direct request to Parallel does not prove Bifrost routing, authentication, or downstream permissions.
On a 401/403, check the Parallel credential, account access, and upstream auth configuration. On a handshake 400, check the configured search overrides. For missing tools, inspect connection health and gateway filtering. Keep existing access controls while diagnosing failures.
Optional: distribute this setup skill through Bifrost
In Skills Repository → New Skill, copy this file's name and description into Details, paste only the Markdown body into the SKILL.md editor, and attach references/gateway-controls.md. For an existing skill, create a new version. Publish an initial version such as 1.0.0, then use Register as Marketplace and the dashboard's installation commands; the plugin name is bifrost-parallel-mcp-setup.
Bifrost serves newly created skills immediately and documents marketplace/download routes as public. Keep deployment credentials and private configuration out of published files. Skill publication is separate from MCP connection setup.
Sources
FAQ
What does the parallel-mcp-setup skill do?
Use when installing, configuring, or troubleshooting an authenticated Parallel Search MCP connection in Bifrost, including gateway-level search settings and downstream tool access.
How do I install parallel-mcp-setup?
Run npx skills add https://github.com/parallel-web/parallel-agent-skills --skill parallel-mcp-setup in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/parallel-mcp-setup/ (personal) or .claude/skills/parallel-mcp-setup/ (project).
Which AI agents can use parallel-mcp-setup?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is parallel-mcp-setup safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains parallel-mcp-setup?
It is published by parallel-web in parallel-web/parallel-agent-skills. It has 1,052 recorded installs.
More from parallel-web/parallel-agent-skills
parallel-deep-research
parallel-web/parallel-agent-skills
ONLY use when user explicitly says 'deep research', 'exhaustive', 'comprehensive report', or 'thorough investigation'. Slower and more expensive than parallel-web-search. For normal research/lookup requests, use parallel-web-search instead. Supports follow-ups with a known prior Task interaction ID.
parallel-web-search
parallel-web/parallel-agent-skills
CLI-backed web search. Use when the user explicitly invokes this skill, needs CLI-only controls or saved JSON output, or no Parallel websearch MCP tool is available. When the bundled Parallel Search MCP is available, prefer its websearch tool for ordinary lookups and current-information queries. Only use parallel-deep-research if the user explicitly requests deep or exhaustive research.
parallel-web-extract
parallel-web/parallel-agent-skills
CLI-backed URL extraction. Use when the user explicitly invokes this skill, needs CLI-only controls or saved JSON output, or no Parallel webfetch MCP tool is available. When the bundled Parallel Search MCP is available, prefer its webfetch tool for ordinary webpages, articles, PDFs, and JavaScript-heavy sites.
parallel-data-enrichment
parallel-web/parallel-agent-skills
Bulk data enrichment. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data. Supports multi-turn: pass --previous-interaction-id from a prior research task to carry context forward.
status
parallel-web/parallel-agent-skills
Check running research task status by run ID
result
parallel-web/parallel-agent-skills
Get completed research task result by run ID
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available