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URL: https://glama.ai/mcp/servers/nathanrcast/planning-center-mcp

⇱ planning-center-mcp by nathanrcast | Glama

Planning Center MCP Server

An MCP (Model Context Protocol) server for Planning Center Online Services. Gives AI agents (Claude, etc.) direct access to your worship plans, song library, teams, and volunteer data.

Includes a built-in AI agent (ask_question) that accepts natural language questions and calls tools automatically using a local Ollama model.

Tools

AI Agent

Tool

Description

ask_question

Ask a natural language question about your PCO data. Uses a local Ollama model to call the appropriate tools and return a human-readable answer.

Services & Plans

Tool

Description

get_service_types

List all service types

get_plans

Paginated plans for a service type (most recent first)

get_plan_items

Get songs, headers, and media in a plan

get_plan_team_members

Get volunteers assigned to a plan

get_plan_details

Get items and team members in one call

Songs & Arrangements

Tool

Description

get_songs

Paginated song library listing

get_song

Get a song by ID or search by title

get_song_schedules

Schedule history for a song

get_arrangements

List arrangements, or get a specific one by ID

get_keys_for_arrangement

Available keys for an arrangement

get_arrangement_attachments

List file attachments (PDFs, audio, etc.)

create_song

Create a new song

Tags

Tool

Description

get_song_tags

List all available song tags by group

assign_tags_to_song

Tag a song by tag name

find_songs_by_tags

Find songs matching tags (AND logic)

File Visibility (Attachment Types)

Tool

Description

get_attachment_types

List org-level file classification types

create_attachment_type

Create custom types (Lead Sheet, Guitar Tab, etc.)

get_team_positions

Get teams, positions, and their attachment type mappings

map_positions_to_attachment_types

Assign which file types a position can see

enable_attachment_types

Toggle position-based file visibility on a service type

Reports (cached data)

Sync

Tool

Description

sync_pco_data

Sync PCO data (incremental by default, full=True for complete re-sync)

get_sync_status

Check when the last sync occurred

get_team_names

All team names from synced data

Song Library

Tool

Description

song_usage_report

Ranked song play counts with optional date range and service type filters

song_detail_report

Full song details: arrangements, key-per-schedule history

song_key_usage_report

Keys ranked by frequency across all songs in a time period

songs_by_key_report

All songs ever played in a specific key (e.g. G, Bb), ranked by count

songs_not_played_report

Songs not played in the last N months — sorted by most recently used before the cutoff

songs_played_together_report

Songs most frequently paired with a given song in the same service

song_retirement_report

Songs that were played frequently in an older window but have since dropped off

service_bpm_flow_report

Tempo (BPM) and key progression across recent services, in song order

Service Plans

Tool

Description

service_plan_report

Recent plans with setlists (including key per song) and team rosters

upcoming_services_report

Upcoming plans with confirmed / pending / declined team members

service_position_report

Songs most commonly used in a given service position (intro, outro, middle)

Volunteers & People

Tool

Description

volunteer_activity_report

Volunteer frequency with optional team and date filters

volunteer_decline_report

Volunteers with the most declined requests, including decline rate

person_song_keys_report

Keys used in plans where a person served, optionally filtered by role

person_song_preferences_report

Songs played when a person served, optionally filtered by role

Related MCP server: ServiceTitan MCP Server

AI Agent: ask_question

The ask_question tool runs a multi-step tool-calling loop using a local Ollama model. It selects and chains the appropriate tools automatically based on your question, then returns a concise, human-readable answer.

How It Works

  1. Your question is sent to the Ollama model along with the schemas for 30 curated read-only tools

  2. The model decides which tools to call and with what parameters

  3. Tool results are fed back to the model

  4. Steps 2–3 repeat (up to 10 iterations) until the model produces a final answer

Configuration

Environment Variable

Default

Description

OLLAMA_URL

http://localhost:11434

Ollama instance URL

AGENT_MODEL

mistral-small3.1

Model to use for tool calling

Any model with tool-calling support works. Larger models (24B+) are significantly more reliable at multi-step reasoning. Tested with mistral-small3.1.

Example Questions

Song Library

What are our top 10 most played songs over the last 6 months?
When did we last play "How Great Is Our God"?
What key do we usually play "Blessed Be Your Name" in?
What songs have we not played in the last 3 months?
What songs have we quietly dropped from rotation this year?

Keys & Setlist Planning

What are the most popular keys we use?
What songs can we do in G?
What songs pair well with "Cornerstone"?
What do we usually open with?
What's our typical tempo arc through a service?

Service Plans

What songs are in this Sunday's service?
Show me the last 3 Sunday morning setlists with keys.
Which upcoming services have open volunteer spots?
What did we play on Easter?

Volunteers & Teams

Who are our most active volunteers over the last 3 months?
Who has been declining a lot of service requests lately?
Who is on the worship team this Sunday?
Which team positions are unfilled for next week?

Person-Specific

What keys does [name] play in when on guitar?
What songs does [name] tend to pick when leading worship?
What teams does [name] serve on?

General

When was the data last synced from Planning Center?
What service types do we have?

Prompting Tips

  • Be specific with time ranges: "last 3 months" or "since January" works better than "recently"

  • Name songs directly: Use the song title as it appears in PCO for best results

  • Ask one thing at a time: Multi-part questions ("top songs AND who played last week") can confuse the model — ask them separately

  • For volunteers, specify the team: "Who is on the band this Sunday?" is clearer than "who is volunteering?"

  • Synced data vs live data: Reports (song usage, volunteer activity, upcoming services) query the local MongoDB cache — run a sync first if your data may be stale. Direct lookups (get_song, get_plans) hit PCO live.

  • The agent won't modify data: It only has access to read-only tools. Write operations (creating songs, assigning tags) must be called directly.

Setup

1. Get PCO API Credentials

Create a Personal Access Token at the PCO Developer Portal.

2. Configure

cp .env.example .env
# Edit .env with your PCO credentials, MongoDB password, and Ollama URL

3. Run with Docker (recommended)

docker compose up -d

The MCP endpoint will be available at http://localhost:8080/mcp.

4. Run Locally (alternative)

pip install .
planning-center-mcp

Requires a running MongoDB instance (set MONGO_URI in .env).

Connect to Claude

Claude Desktop

Add to your claude_desktop_config.json:

{
 "mcpServers": {
 "planning-center": {
 "type": "http",
 "url": "http://localhost:8080/mcp"
 }
 }
}

Claude Code

Add to your project's .mcp.json:

{
 "mcpServers": {
 "planning-center": {
 "type": "http",
 "url": "http://localhost:8080/mcp"
 }
 }
}

Architecture

PCO API ← pypco ─┬─ services.py (direct API tools)
 └─ sync.py ──── MongoDB ─┬─ queries.py ─ reports.py (cached reports)
 └─ llm.py (embeddings, optional AI summaries)

agent.py ─ Ollama (tool-calling loop) ─ dispatches to any registered tool

  • Direct tools (services.py): Hit the PCO API live. No cache needed.

  • Report tools (reports.py): Query local MongoDB for aggregated data. Run sync_pco_data to refresh.

  • Sync (sync.py): Incremental by default — only fetches records updated since the last sync.

  • Agent (agent.py): Accepts a natural language question, builds an Ollama tool-calling loop over 30 curated read-only tools, and returns a plain-text answer.

  • AI features (llm.py): Optional. Enables AI-generated summaries via Ollama.

Development

pip install -e ".[dev]"
pytest
A
license - permissive license
-
quality - not tested
C
maintenance

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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