shunbao-p/deer-flow-s/runtime-tool-builder

Claude Code and agent skill by shunbao-p/deer-flow-s

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Checking that the SKILL.md file is still in the repository.

Create, test, install, and register a minimal Python stdio MCP server during agent runtime when the task needs a missing execution capability and evaluate_tool_gap has already confirmed a TOOL_GAP. Use only after existing skills and current tools are not enough.

Install

npx vskill@latest install shunbao-p/deer-flow-s/runtime-tool-builder

SKILL.md

Runtime Tool Builder

Use this skill only after the lead agent has already confirmed that the current gap is a real tool gap. Before using this skill, the agent should have already called evaluate_tool_gap and received TOOL_GAP.

Hard Guardrails

Never use this skill when any of the following is true:

  • An existing skill already covers the task well enough
  • Existing normal tools or current MCP tools are already sufficient
  • The request is one-off, temporary, ambiguous, or still missing key requirements
  • The task only needs a better workflow description rather than a new execution capability

Your goal is not to build a general platform. Your goal is to build the smallest MCP server that adds the missing execution capability safely.

Scope

First version supports only:

  • Python
  • stdio
  • one MCP server
  • one tool

Do not improvise:

  • Node
  • HTTP
  • SSE
  • multiple tools

Working Location

Create the draft MCP server inside the current thread workspace, not directly inside custom-mcp-servers.

Recommended layout:

text
/mnt/user-data/workspace/runtime-tools/<tool-name>/
server.py
tool_impl.py
requirements.txt
README.md

Step 1: Reconfirm Preconditions

Before writing anything, verify all of these:

  1. Existing skills are not sufficient.
  2. Existing normal tools and current MCP tools are not sufficient.
  3. evaluate_tool_gap already returned TOOL_GAP.
  4. The task really needs a missing execution capability, not just a reusable workflow.
  5. The requested capability is stable enough to wrap as one minimal tool.

If any item fails, stop and do not create a tool.

Step 2: Copy the Fixed Template Shape

Use this exact minimal file set:

  • server.py
  • tool_impl.py
  • requirements.txt
  • README.md

Preferred canonical template location for developers:

text
tool-templates/python-mcp-server/

At runtime, reproduce the same structure in the thread workspace.

Keep server.py as close as possible to this fixed shape:

python
from mcp.server.fastmcp import FastMCP
from tool_impl import run_tool as run_tool_impl
mcp = FastMCP("runtime-python-mcp-template")
@mcp.tool()
def run_tool(input_text: str) -> str:
return run_tool_impl(input_text)
if __name__ == "__main__":
mcp.run(transport="stdio")

Default tool_impl.py shape:

python
def run_tool(input_text: str) -> str:
return f"Template tool received: {input_text}"

Default requirements.txt:

text
mcp>=1.0.0

Step 3: Customize Only the Business Logic

Prefer to modify only:

  • tool_impl.py
  • README.md

Only change requirements.txt when the capability truly needs extra runtime dependencies. Avoid changing server.py unless the fixed wrapper is definitely insufficient.

Rules for the first version:

  • keep a single function named run_tool
  • keep one string input unless the task absolutely requires more structure
  • keep the return value simple and deterministic

Step 4: Minimal Validation Before Installation

Before installing anything, run at minimum:

bash
python -m py_compile /mnt/user-data/workspace/runtime-tools/<tool-name>/server.py /mnt/user-data/workspace/runtime-tools/<tool-name>/tool_impl.py
python -c "from pathlib import Path; import sys; sys.path.insert(0, '/mnt/user-data/workspace/runtime-tools/<tool-name>'); from tool_impl import run_tool; print(run_tool('ping'))"

Confirm at minimum:

  • server.py parses
  • tool_impl.py parses
  • run_tool('ping') returns a sensible result

If validation fails, fix the draft before installing.

Step 5: Install the MCP Server Project

After validation succeeds, call:

python
install_custom_mcp_server(
path="/mnt/user-data/workspace/runtime-tools/<tool-name>",
tool_name="<tool-name>",
source="runtime_auto_create",
)

Rules:

  • Install only through install_custom_mcp_server
  • Never write directly into custom-mcp-servers
  • If installation fails, read the error, fix the workspace draft, and retry only when the fix is concrete

Step 6: Register and Validate It

After installation succeeds, call:

python
register_custom_mcp_server(
tool_name="<tool-name>",
description="Runtime-generated MCP server for <tool-name>",
source="runtime_auto_create",
)

Rules:

  • Register only through register_custom_mcp_server
  • Never edit extensions_config.json directly
  • This step must finish successfully before you tell the user the tool is available

Step 7: After Registration

After successful registration:

  1. Tell the user the new tool was created, installed, and registered.
  2. State that it will be available in later messages in the same thread.
  3. Do not assume the current turn has already rebound the new tool schema.
  4. Keep the product semantics consistent: current turn installs, next message uses.

Quality Bar

The generated MCP server should be:

  • minimal
  • understandable
  • focused on one missing execution capability
  • safer than ad hoc bash glue for repeated use

If you cannot meet that bar, do not create a tool.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
54
Repository forks
2
7-day trend score
20

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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