Add Mcp Skill

add-mcp-skill

> Status: v0.10.0 — folder-pattern reference written (the most important component) and the runnable MCP server skeleton (templates/mcp-skeleton/) is now included (FastMCP server.py with read tools always-on and write tools off by default). Write tools are safe-by-default: off unless MCP_WRITE_TOOLS=on, and when on they OPEN A PR for human review rather than pushing to main (the server feeds untrusted synced data to an agent — see [references/mcp-github-writeback.md](references/mcp-github-writeback.md), "Security posture"). Docker/compose/deploy and a working example-sales skill ship too. Step-by-step playbook for adding a skill to an existing MCP server still skeletal.

What this skill does

Extends the client's MCP server with a new "skill" — a callable tool plus its context. After this skill runs, an AI agent connected to the MCP server can answer natural-language questions about the new data domain, generate the right SQL, and execute it against BigQuery.

The pattern mirrors pol-cc/skills-sapiens (the reference MCP deployment): one MCP server, multiple skills, each scoped to a domain (sales, finance, marketing, etc.).

Preflight

if [ ! -f .agentic-data-engineer.json ]; then
  echo "[abort] not a managed MDS deployment"
  exit 1
fi

jq -e '.stack.mcp == true' .agentic-data-engineer.json > /dev/null || {
  echo "[abort] this MDS doesn't have an MCP server"
  echo "run Phase 3 of create-mds first"
  exit 1
}

Anatomy of an MCP skill

Each skill in the MCP server is a folder under mcp-server/skills//:

/
├── descriptor.json    declares which BQ datasets/tables this skill can read
├── context.md         business context the LLM needs to write correct SQL
├── schema.md          per-table column documentation, gotchas, joins
└── examples.sql       example queries (the LLM learns the pattern)

The MCP server exposes one generic run_bq_query tool and uses the per-skill files as the context the calling agent loads before composing a query.

Playbook outline

Phase A — Define the skill scope

Ask the user:

1. What domain? (sales, finance, marketing, operations, HR, etc.)
2. Which BigQuery tables/datasets are in scope?
3. What kinds of questions should the skill answer?

Phase B — Write the skill files

1. descriptor.json — declare allowed tables, max bytes per query, max rows.
2. context.md — business glossary: what is a "customer" in this client's world, how are channels classified, etc.
3. schema.md — for each table, the meaningful columns + gotchas (e.g. "amount is signed for refunds", "vendor_code is NULL for off-catalog").
4. examples.sql — three to five canonical queries the LLM can pattern-match against.

See [templates/mcp-skeleton/](templates/mcp-skeleton/) — a runnable starter (FastMCP server.py, requirements.txt, Dockerfile, docker-compose.yml, .env.example, deploy.sh) shipping with a working skills/example-sales/ skill. Copy a sibling of example-sales, edit the four files for the new domain.

Phase C — Deploy

1. Commit the new skill folder to the client repo.
2. On the VPS, pull the change and restart the MCP container.
3. Verify the skill is listed via list_skills() from an MCP client.

Phase D — Verify

Connect to the MCP server from claude.ai or Claude Code and ask a representative question. Confirm the LLM produces correct SQL grounded in the context files.

References

Folder pattern (complete):

• [references/mcp-skill-folder-pattern.md](references/mcp-skill-folder-pattern.md) — the four-file structure (descriptor.json + context.md + schema.md + examples.sql), quality bars per file, iteration loop, multi-skill rules

Background (in create-mds Phase 3):

• [../create-mds/references/mcp-server-architecture.md](../create-mds/references/mcp-server-architecture.md) — how the MCP server uses these files

Templates (complete):

• [templates/mcp-skeleton/](templates/mcp-skeleton/) — runnable MCP server starter: FastMCP server.py (always-on read tool + list_skills/get_skill_context, plus the two write tools that register only when MCP_WRITE_TOOLS=on and then open a PR rather than push to main — _open_pr branches, commits, pushes the branch, and calls the GitHub PR API; with the SELECT-only / table-allowlist / path-traversal / sync-before-write / branch-cleanup-on-failure safety logic), requirements.txt, Dockerfile, docker-compose.yml, .env.example (MCP_WRITE_TOOLS=off default), deploy.sh, README.md, and a working skills/example-sales/ skill (descriptor.json + context.md + schema.md + examples.sql)

Still to be written:

• references/skills-sapiens-reference.md — annotated walkthrough of the production reference deployment's first skill

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: pol-cc
• Source: pol-cc/agentic-data-engineer
• License: MIT

Install and usage instructions live in the source repository linked above.