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Home/Servers/MCP Server Decisions

MCP Server Decisions

I
by io.github.Roberton003

Decision tracking with prediction validation and outcome gates for AI agents

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Local command
uvx mcp-server-decisions==1.0.2
Credentials from the publisher
  • MCP_DECISIONS_LOG_PATH β€” Path to decisions log file (default: ~/.local/share/mcp-decisions/decisions_log.json)
Provider
Claude Code
MCP_DECISIONS_LOG_PATH=<value> claude mcp add io.github.roberton003-mcp-server-decisions -- uvx mcp-server-decisions==1.0.2

Run in a terminal.

Claude Desktop
{
  "mcpServers": {
    "io.github.roberton003-mcp-server-decisions": {
      "command": "uvx",
      "args": [
        "mcp-server-decisions==1.0.2"
      ],
      "env": {
        "MCP_DECISIONS_LOG_PATH": "<value>"
      }
    }
  }
}

claude_desktop_config.json

Step-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

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Host a copy with our keys and logs.

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Questions

How do I install MCP Server Decisions?
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
Does MCP Server Decisions need an API key?
The publisher lists these environment variables: MCP_DECISIONS_LOG_PATH.
What transport does MCP Server Decisions use?
MCP Server Decisions runs locally over stdio.
When was MCP Server Decisions last checked?
Last checked 2026-10-16. Decision tracking with prediction validation and outcome gates for AI agents

Decision tracking with prediction validation and outcome gates for AI agents

🧠 MCP Server: Decisions

An open-source MCP server that helps teams record architectural decisions, connect them to testable predictions, and validate outcomes over time. It gives AI agents and developers a lightweight, auditable memory for technical choices.

Architectural Decision Feedback Loop with Outcome Gates

✨ Project Highlights

  • Outcome-linked decisions β€” connect each technical choice to measurable predictions and observed results.
  • In-band outcome gates β€” tool responses identify predictions that still need validation before the work is considered complete.
  • Portable storage β€” append-only JSONL keeps the log inspectable, easy to back up, and free from database setup.
  • Zero runtime dependencies β€” Python's standard library is enough to run the server.
  • MCP-native interface β€” expose decision tracking through JSON-RPC over stdio to MCP-compatible clients.
  • Technology feedback β€” aggregate validated outcomes to inform future technology choices.

🧰 Technical Stack

LayerTechnology
ProtocolModel Context Protocol over JSON-RPC 2.0
RuntimePython 3.10+
StorageAppend-only JSONL file
PackagingPyPI / Hatchling
TestingBuilt-in self-test command
LicenseMIT

πŸ”„ Architecture

flowchart TD
    A[MCP client or AI agent] --> B[JSON-RPC over stdio]
    B --> C[mcp-server-decisions]
    C --> D[Record decision]
    C --> E[Attach prediction]
    C --> F[Record outcome]
    C --> G[Query decisions and technology history]
    D --> H[(Append-only JSONL log)]
    E --> H
    F --> H
    G --> H
    F --> I[Validation status and accuracy]
    I --> J[Future technical decisions]

πŸ“Œ What It Provides

The server exposes four tools:

ToolPurpose
record-decisionStore the problem, chosen solution, alternatives, technologies, and predictions.
record-predictionAdd a measurable prediction to an existing decision.
record-outcomeRecord the observed result and classify the prediction as success, partial success, or failure.
query-decisionsSearch decisions by keyword, technology, domain, or result limit.

Example flow

Decide β†’ Predict β†’ Implement β†’ Measure β†’ Validate β†’ Learn

A decision can produce an outcome-gate reminder such as:

{
  "decision_id": "DEC-2026-0001",
  "status": "OK",
  "OUTCOME_GATE": "2 prediction(s) still lack outcomes."
}

The reminder is a workflow signal, not a claim about adoption or measured impact. See the Outcome Gate Pattern for the design and trade-offs.

πŸ“Š Current Project Status

AreaStatus
Decision, prediction, and outcome trackingAvailable
Outcome-gate remindersAvailable
Technology performance reportAvailable
PyPI packagePublished as 1.0.2
External adoption metricsNot collected yet
Web UI and notificationsRoadmap

The project is early-stage. Contributions, examples from real projects, and feedback are welcome.

πŸš€ Setup

Prerequisites

  • Python 3.10 or newer
  • An MCP-compatible client

Install from PyPI

python3 -m pip install mcp-server-decisions

Run the self-test

python3 -m pip install -e .
python3 server.py --selftest

Configure an MCP client

{
  "mcpServers": {
    "mcp-server-decisions": {
      "command": "mcp-server-decisions"
    }
  }
}

For client-specific configuration and troubleshooting, see Client Integrations. For a guided first run, see Quick Start.

Configure the log path

By default, the server writes to ~/.local/share/mcp-decisions/decisions_log.json. Set MCP_DECISIONS_LOG_PATH to use another file:

MCP_DECISIONS_LOG_PATH=/path/to/decisions.json mcp-server-decisions

πŸ—‚οΈ Project Structure

.
β”œβ”€β”€ server.py                         # MCP server and tool implementations
β”œβ”€β”€ scripts/                          # Reports derived from the decision log
β”œβ”€β”€ docs/                             # Architecture, examples, and integrations
β”œβ”€β”€ .github/ISSUE_TEMPLATE/           # Reusable bug and feature templates
β”œβ”€β”€ CONTRIBUTING.md                   # Development and contribution workflow
β”œβ”€β”€ QUICKSTART.md                     # Guided setup and first decision
β”œβ”€β”€ server.json                       # MCP Registry metadata
β”œβ”€β”€ pyproject.toml                    # PyPI package metadata
└── LICENSE                           # MIT license

πŸ“š Documentation

  • Quick Start β€” install and record a first decision.
  • Client Integrations β€” configure MCP clients.
  • Detailed Examples β€” JSON-RPC requests and responses.
  • Architecture & Design β€” storage, IDs, scoring, and trade-offs.
  • Outcome Gate Pattern β€” the reusable feedback-loop pattern.
  • Contributing β€” propose fixes, features, and documentation.

πŸ›£οΈ Roadmap

  • Core decision, prediction, and outcome tracking
  • Outcome-gate reminders
  • Technology performance reporting
  • Web UI for browsing and searching decisions
  • Notifications for low prediction accuracy
  • Reusable decision templates and domain patterns

🀝 Contributing

Issues and pull requests are welcome. Start with CONTRIBUTING.md, run the self-test, and explain the problem or use case in the pull request.

πŸ“„ License

MIT Β© 2026 Roberto Nascimento

How do I install MCP Server Decisions?
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
Does MCP Server Decisions need an API key?
The publisher lists these environment variables: MCP_DECISIONS_LOG_PATH.
What transport does MCP Server Decisions use?
MCP Server Decisions runs locally over stdio.
When was MCP Server Decisions last checked?
Last checked 2026-10-16. Decision tracking with prediction validation and outcome gates for AI agents

No tool list yet. Remote servers get one after the first health check.

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