Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@33GOD Pipeline MCP Hub list available domains"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A multi-domain MCP server that fronts many tool domains (Plane, BloodBank, ticket-lifecycle, …) behind one small, stable surface — so an agent isn't flooded with hundreds of tool schemas / millions of tokens.
Served at https://mcp.delo.sh/mcp (header-PAT auth).
The hub exposes only three tools to clients:
Tool | Purpose |
| List available domains (name, description, tool_count). Start here. |
| Load + list one domain's tools, each with its |
| Invoke a specific domain tool; |
Underlying domain tools are held in an internal registry and are never advertised
directly, so the per-request tool-schema cost stays tiny and constant. This works on
any MCP client regardless of notifications/tools/list_changed support.
Trade-off: domain tools are invoked through
call_domain_tool(...)rather than as native function calls. The hub validates arguments against the real tool schema and returns clear errors. (A future "native passthrough" mode could expose real prefixed tools for clients that already defer schemas, like Claude Code.)
Related MCP server: fastmcp-gateway
plane — reuses plane-mcp-server's tools in-process (work items, projects, cycles,
epics, etc.). Auth + Plane API calls use the caller's PAT from the request context.
bloodbank (Phase 1) — publish/trace events on the BloodBank NATS bus.
lifecycle (Phase 2) — opinionated ticket state machine (pull → triage → … → done).
HTTP mode uses the same header scheme as plane-mcp-server:
Authorization: Bearer <PLANE_PAT>
x-workspace-slug: <workspace-slug>
# Install the hub + the plane domain dependency (from the local checkout)
uv pip install -e .
uv pip install -e ../../plane-mcp-server
# stdio (no auth; Plane tools use env fallback)
PLANE_API_KEY=... PLANE_WORKSPACE_SLUG=... python -m mcp_hub stdio
# http (header-PAT auth) on :8998 at /mcp
PLANE_INTERNAL_BASE_URL=http://localhost:8000 python -m mcp_hub httpDocker stack at ~/docker/stacks/ai/pipeline-mcp-hub/. It builds this repo and pulls the
plane-mcp-server source via a named build context, joins the proxy network, and is
routed by Traefik at Host(mcp.delo.sh). *.delo.sh already wildcards through the
Cloudflare tunnel → Traefik.
cd ~/docker/stacks/ai/pipeline-mcp-hub
docker compose up -d --buildRequires Docker Compose ≥ v2.17 (for build.additional_contexts).
Var | Default | Purpose |
|
| Listen port inside the container |
| — | Server-to-server Plane API base; set to |
|
| Fallback Plane API base |
Phase 0 needs no secrets — the Plane PAT arrives per-request in the client's headers.
Invoke a specific tool within a domain and return its result.
arguments is a JSON object that must conform to that tool's input_schema
(from list_domain_tools(domain)). Example:
call_domain_tool("plane", "list_projects", {})
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | ||
| domain | Yes | ||
| arguments | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It says 'invoke' which implies potential side effects, but it does not disclose any behavioral traits such as permissions, rate limits, error handling, or what happens if the domain/tool is invalid. The description is too generic to inform the agent about behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a concise example. Every sentence is informative, with no fluff or repetition. The structure is front-loaded with the core purpose and then provides an illustrative example. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no output schema, and a sibling with a different purpose, the description is moderately complete. It explains the invocation pattern and the argument requirement. However, it does not describe the return value format, possible errors, or how to obtain the input schema for a specific tool. The example helps but does not cover all contextual gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It explains that 'arguments' must conform to the tool's input schema and gives an example. However, it does not describe what 'domain' and 'tool' represent (e.g., they must match values from list_domains/list_domain_tools). The example partially compensates, but the meaning of all parameters is not fully clarified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it invokes a specific tool within a domain and returns its result. The verb 'invoke' and the resource 'tool within a domain' are specific, and it distinguishes from the sibling 'list_domains', which only lists domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides an example and notes that arguments must conform to the tool's input schema from list_domain_tools. However, it does not explicitly state when to use this tool vs alternatives, nor does it mention prerequisites (e.g., needing to call list_domain_tools first). Usage context is implied but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the tool domains available in this hub. START HERE.
Each domain bundles many underlying tools that are intentionally NOT loaded into your context yet. Returns each domain's name, title, description, tool_count, and load status. To use a domain, call list_domain_tools(domain) to load its tool schemas, then invoke a tool with call_domain_tool(domain, tool, arguments).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description explains that tools are intentionally not loaded into context until requested, and mentions the return fields. It does not explicitly state read-only nature, but the behavior is implied. Minor gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences, front-loaded with 'START HERE'. Every sentence adds essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter list tool with an output schema (implied), the description fully covers purpose, usage, and return value composition. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so schema coverage is 100%. The description does not add parameter-specific meaning beyond stating the function.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List the tool domains' with a specific verb and resource. It distinguishes itself from the sibling tool call_domain_tool by explaining this is the starting point for discovering domains before using them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'START HERE' and provides a step-by-step guide: list domains, then use list_domain_tools then call_domain_tool. This gives clear when-to-use and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0call_domain_toollist_domainsScored across 2 tools
The two tools have completely distinct purposes: list_domains for discovery and call_domain_tool for invocation. No overlap at all.
Both tools follow a consistent verb_noun snake_case pattern (list_domains, call_domain_tool), making them predictable and clear.
With only 2 tools, the set feels thin, but for a hub server that delegates to domains, this minimal surface is acceptable and focused.
The hub is missing the list_domain_tools tool, which is advertised in list_domains' description. Without it, users cannot discover and invoke tools within domains, causing a critical gap.
MCP server for progressive tool usage at any scale (see https://klavis.ai)
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
The OpenRouter for tools. One MCP connection gives any AI agent 254 hosted tools, pay per call.
Search, vet & assemble MCP servers from your agent: verified tools, risk labels, and trust scores.
