ollama-agents-mcp

MCP server that scaffolds and runs a local Ollama "sub-agent" pipeline with three role prompts (collector, writer, reviewer) and run_agents.sh.

What This MCP Actually Does

This MCP provides a repeatable local pipeline where the same local LLM is run multiple times with different roles, and each output is saved as a file.

Think of it as a small offline workflow engine:

1. Collector agent

• Input: messy notes, logs, metrics, tickets

• Output: normalized JSON plus short evidence notes

• Purpose: reduce invention and force structured extraction

2. Writer agent

• Input: only the collector JSON

• Output: polished report in Markdown

• Purpose: consistent report structure and faster drafting

3. Reviewer agent

• Input: JSON plus report

• Output: PASS/FAIL plus issues and required fixes

• Purpose: quality gate for contradictions, omissions, and vague claims

These are separate role runs with a shared workspace. They are not autonomous background workers.

Related MCP server: mcp-brain

Pipeline Artifacts (What You Get Every Run)

Under work/, each run creates:

• 01_collector_<timestamp>_tryN.md: raw collector output

• 02_data.json: normalized source-of-truth data used by writer and reviewer

• 04_report_<timestamp>.md: generated draft report

• 06_review_<timestamp>.md: PASS/FAIL review and required fixes

Why this matters:

• if report text looks wrong, inspect 02_data.json first

• if JSON is wrong, inspect collector output and input notes

• reviewer output tells you exactly what to fix before sharing

Typical Operator Flow

1. Paste current month notes into work/input.txt (incidents, changes, metrics, risks, next plan)

2. Run setup once (or when role prompts/scripts change)

3. Run pipeline

4. Open 02_data.json, 04_report_*.md, and 06_review_*.md

5. Apply reviewer-required fixes and re-run if needed

Core Use Cases

1. Monthly or weekly ops reports

• Input: incidents, key metrics, change summary

• Output: normalized data JSON, final report, quality review

• Benefit: consistent month-over-month format with fewer manual errors

2. Post-incident and RCA packs

• Collector extracts timeline, impact, mitigation, and actions

• Writer drafts RCA document

• Reviewer checks missing root cause, owners, due dates, and unsupported claims

3. Change review and maintenance summaries

• Turn change notes and outcomes into a standard "what changed / risk / rollback / verification" artifact

4. Messy input to clean artifact conversion

• Examples: meeting notes to minutes, ticket dumps to executive summaries, log snippets to hypotheses and next checks

5. Offline or privacy-sensitive operations

• Keeps processing local; no cloud dependency for the pipeline itself

Why Split Into Roles Instead Of One Prompt

Single large prompts often mix extraction and writing, miss sections, and drift in style over time.

Role separation gives:

• separation of concerns

• reusable monthly process

• audit trail (02_data.json as source of truth)

• quality gate (reviewer can block weak drafts)

Non-Goals

• It does not auto-pull Grafana/Prometheus/Jira data unless you add separate scripts or API integrations.

• It does not run roles in parallel by default.

• It does not know your environment automatically; you still provide inputs.

Quick Start In 60 Seconds

Prereqs:

• ollama installed and running

• python3 available

• MCP server configured with env vars:

• OLLAMA_AGENTS_MCP_STATE_DIR=<MCP_DATA_ROOT>/ollama-agents-mcp

• OLLAMA_AGENTS_BASE_DIR=<MCP_DATA_ROOT>/ollama-agents-mcp/workspace

Then run:

1. health_check()

2. setup_default_environment()

3. list_agent_roles() (expect collector, writer, reviewer)

4. run_default_pipeline()

Expected outputs under <MCP_DATA_ROOT>/ollama-agents-mcp/workspace/work:

• 01_collector_*.md

• 02_data.json

• 04_report_*.md

• 06_review_*.md

Optional hardening on run:

• run_ollama_agents_pipeline(pipeline_input_file="work/input.txt", collector_retries=3, enforce_schema=true)

Intuitive Commands (Short Aliases)

Use these for day-to-day work:

• setup_default_environment()

• run_default_pipeline()

• setup_and_run_default_pipeline()

Use full commands only when overriding models/behavior:

• setup_ollama_agents_environment(...)

• run_ollama_agents_pipeline(...)

Guided Inputs (Options + Defaults)

If you want selectable options with default-enter behavior:

1. list_pipeline_run_options()

• returns available work/* input files

• returns currently installed Ollama models from ollama list

• returns defaults used by guided run

2. run_pipeline_guided(...)

• leave fields blank to use defaults

• set only fields you care about (for example collector_model)

• input_file supports:

• single file: work/input.txt

• multiple files: work/a.txt,work/b.txt

• folder: work/ (recursively combines files into one generated input)

Example:

• run_pipeline_guided()

• run_pipeline_guided(collector_model="deepseek-r1:latest")

• run_pipeline_guided(input_file="work/input.txt")

• run_pipeline_guided(input_file="work/incident.txt,work/changes.txt")

• run_pipeline_guided(input_file="work/")

Path Placeholders

• <MCP_STUFF_ROOT>: parent MCP checkout root (example: /Volumes/Data/_ai/_mcp/mcp_stuff)

• <MCP_DATA_ROOT>: persistent MCP runtime data root (example: /Volumes/Data/_ai/_mcp/mcp-data)

What It Sets Up

Tool setup_ollama_agents_environment supports actions:

• setup: scaffold environment files

• run: run existing pipeline only

• setup_and_run: scaffold and then run

When setup is used, it creates:

• <MCP_DATA_ROOT>/ollama-agents-mcp/workspace/agents/collector.md

• <MCP_DATA_ROOT>/ollama-agents-mcp/workspace/agents/writer.md

• <MCP_DATA_ROOT>/ollama-agents-mcp/workspace/agents/reviewer.md

• <MCP_DATA_ROOT>/ollama-agents-mcp/workspace/run_agents.sh

• <MCP_DATA_ROOT>/ollama-agents-mcp/workspace/work/input.txt (optional)

run_agents.sh executes the 3-stage flow:

1. Collector extracts structured JSON

2. Writer produces monthly report from JSON only

3. Reviewer validates report consistency against JSON

Implemented MCP Tools

• health_check

• setup_ollama_agents_environment

• setup_default_environment

• setup_and_run_default_pipeline

• list_pipeline_run_options

• run_ollama_agents_pipeline

• run_default_pipeline

• run_pipeline_guided

• run_role_agent

• list_agent_roles

• get_agent_role_prompt

• upsert_agent_role_prompt

• delete_agent_role_prompt

Data Root Policy

Runtime state for this MCP is persisted under:

• <MCP_DATA_ROOT>/ollama-agents-mcp

Configure with env var:

• OLLAMA_AGENTS_MCP_STATE_DIR

• OLLAMA_AGENTS_BASE_DIR (optional override for workspace path)

The server stores the latest action manifest in last_action.json in this state dir.

Local Setup

cd <MCP_STUFF_ROOT>/ollama-agents-mcp
./bootstrap.sh

Run

cd <MCP_STUFF_ROOT>/ollama-agents-mcp
./venv/bin/python run_server.py

Codex Config Example

~/.codex/config.toml

[mcp_servers.ollama-agents-mcp]
command = "bash"
args = ["-lc", "mkdir -p <MCP_DATA_ROOT>/ollama-agents-mcp && cd <MCP_STUFF_ROOT>/ollama-agents-mcp && exec ./venv/bin/python run_server.py"]

[mcp_servers.ollama-agents-mcp.env]
OLLAMA_AGENTS_MCP_STATE_DIR = "<MCP_DATA_ROOT>/ollama-agents-mcp"
OLLAMA_AGENTS_BASE_DIR = "<MCP_DATA_ROOT>/ollama-agents-mcp/workspace"

Claude Code Config Example

~/.claude.json

{
  "mcpServers": {
    "ollama-agents-mcp": {
      "type": "stdio",
      "command": "bash",
      "args": [
        "-lc",
        "mkdir -p <MCP_DATA_ROOT>/ollama-agents-mcp && cd <MCP_STUFF_ROOT>/ollama-agents-mcp && exec ./venv/bin/python run_server.py"
      ],
      "env": {
        "OLLAMA_AGENTS_MCP_STATE_DIR": "<MCP_DATA_ROOT>/ollama-agents-mcp",
        "OLLAMA_AGENTS_BASE_DIR": "<MCP_DATA_ROOT>/ollama-agents-mcp/workspace"
      }
    }
  }
}

Example Tool Usage

Fast path (recommended):

• setup_default_environment()

• run_default_pipeline()

• setup_and_run_default_pipeline()

Guided path (option listing + defaults):

• list_pipeline_run_options()

• run_pipeline_guided()

Create environment only (uses default workspace under <MCP_DATA_ROOT>/ollama-agents-mcp/workspace):

• setup_ollama_agents_environment(action="setup")

Run existing pipeline only:

• setup_ollama_agents_environment(action="run", pipeline_input_file="work/input.txt")

Create environment and pull models:

• setup_ollama_agents_environment(action="setup", pull_models=true)

Setup and run in one call:

• setup_ollama_agents_environment(action="setup_and_run", pull_models=true, pipeline_input_file="work/input.txt")

Run pipeline directly:

• run_ollama_agents_pipeline(pipeline_input_file="work/input.txt")

• run_ollama_agents_pipeline(pipeline_input_file="work/input_a.txt,work/input_b.txt")

• run_ollama_agents_pipeline(pipeline_input_file="work/")

• run_ollama_agents_pipeline(pipeline_input_file="work/input.txt", collector_retries=3, enforce_schema=true)

Run a single role directly (works for future added roles too):

• run_role_agent(role="collector", input_file="work/input.txt", model="deepseek-r1:latest")

List current role prompts:

• list_agent_roles()

Read one role prompt:

• get_agent_role_prompt(role="collector")

Add a new role prompt (future expansion):

• upsert_agent_role_prompt(role="analyst", prompt="ROLE: Analyst...")

Delete a role prompt:

• delete_agent_role_prompt(role="analyst", confirm=true)

Notes

• Requires local python3 and ollama.

• Model pulls and pipeline execution can take several minutes depending on model size and hardware.

• Pipeline hardening includes collector retry and fallback JSON extraction when fenced blocks are missing.

• enforce_schema=true validates collector JSON contains keys: incidents,changes,metrics,risks,next_month_plan.

Local Customization Tracking

• Local machine-specific integration, client wiring, and operational state are tracked under the external data root.

• Local metadata path: /Volumes/Data/_ai/_mcp/mcp-data/<name>/meta

• Repo-side capability contract is in docs/local-capability/.

• Secrets are never stored in repo docs; only variable names and loading locations are documented.

Local Enhancements Capture (2026-03-13)

• Captured current local changes, configuration updates, and operational enhancements for GitHub publication.

• Includes synchronization with sub-repo link updates where applicable.

• Cross-reference local docs and capability notes added in this repository.