# io.github.dingdawg/dingdawg-planning-agent (npm · dingdawg-planning-agent)

Planning AI. Sprint estimation, task breakdown, risk analysis. Learns your velocity.

- Trust score: 67/100 (medium)
- Change this week: +21
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `dingdawg-planning-agent`: 67/100 (this document), [markdown](https://verifymcp.io/servers/dingdawg-dingdawg-planning-agent/dingdawg-planning-agent.md), [page](https://verifymcp.io/servers/dingdawg-dingdawg-planning-agent/dingdawg-planning-agent)

## Channel facts

- Registry: `npm`
- Package: `dingdawg-planning-agent`
- Version: `2.0.6`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 87/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 27 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 66/100
  - AI-judged instruction clarity (good).
  - Tool/resource definitions use about 425 tokens (~70/item across 6 items; 6 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add dingdawg-dingdawg-planning-agent -- npx -y dingdawg-planning-agent
```

### Codex

```bash
codex mcp add dingdawg-dingdawg-planning-agent -- npx -y dingdawg-planning-agent
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "dingdawg-dingdawg-planning-agent": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "dingdawg-planning-agent"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add dingdawg-dingdawg-planning-agent --command npx --arg -y --arg dingdawg-planning-agent
```

### Hermes

```yaml
mcp_servers:
  dingdawg-dingdawg-planning-agent:
    command: "npx"
    args: ["-y", "dingdawg-planning-agent"]
```

### Other

```json
{
  "mcpServers": {
    "dingdawg-dingdawg-planning-agent": {
      "command": "npx",
      "args": [
        "-y",
        "dingdawg-planning-agent"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 67, +4)

- [functional improvement] Stability: unverified → 0.27

### 2026-08-02 (score 63, +42)

- [security regression] Provenance: unverified → fail
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Install scripts: unverified → pass
- [security improvement] Malware scan: unverified → pass
- [security] Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window).
- [functional improvement] License: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Schema quality: unverified → good
- [functional] Licence: MIT

### 2026-07-31 (score 21, +15)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 6, −40)

- [security regression] Malware scan: pass → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional] First check of Schema quality: unverified

### 2026-07-27 (score 46)

First indexed and scored.

## MCP tools (6)

### `plan_project` (~82 tokens)

Generate a structured project plan with phases, milestones, dependencies, and resource allocation. Requires DINGDAWG_API_KEY for LLM-powered planning.

Input parameters:

- `deadline` (string): Target deadline (YYYY-MM-DD)
- `methodology` (string): Project methodology
- `project_description` (string, required): Describe the project to plan
- `team_size` (number): Team size

### `estimate_effort` (~73 tokens)

Free AI effort estimation with complexity scoring and PERT ranges. Deep LLM-powered subtask breakdown and confidence analysis with API key.

Input parameters:

- `context` (string): Project context, tech stack, constraints
- `task` (string, required): Task or feature to estimate
- `team_experience` (string): Team experience level

### `risk_analyze` (~61 tokens)

Free AI project risk analysis with risk matrix scoring. Deep LLM-powered mitigation strategies and contingency plans with API key.

Input parameters:

- `known_risks` (string): Known risks or concerns
- `project_description` (string, required): Project or initiative to analyze for risks

### `decision_matrix` (~69 tokens)

AI-powered structured decision making with weighted scoring, bias detection, and assumption tracking. Requires DINGDAWG_API_KEY.

Input parameters:

- `criteria` (string): Evaluation criteria (comma-separated)
- `decision` (string, required): Decision to evaluate
- `options` (string, required): Options to compare (comma-separated or described)

### `retrospective` (~80 tokens)

AI project retrospective — what went well, what didn't, action items, pattern identification. Requires DINGDAWG_API_KEY.

Input parameters:

- `project` (string, required): Project name or description
- `team_feedback` (string): Team feedback or notes
- `went_poorly` (string): What went poorly
- `went_well` (string): What went well

### `standup_summary` (~60 tokens)

AI standup summary from team updates — blocker detection, dependency alerts, velocity tracking. Requires DINGDAWG_API_KEY.

Input parameters:

- `sprint_goal` (string): Current sprint goal
- `updates` (string, required): Team standup updates (paste all updates)

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/dingdawg-dingdawg-planning-agent/dingdawg-planning-agent#diagnostics

## Score history

- 2026-08-03: 67
- 2026-08-02: 63
- 2026-08-01: 21
- 2026-07-31: 21
- 2026-07-30: 6
- 2026-07-28: 46
- 2026-07-27: 46

## Links

- npm package: https://www.npmjs.com/package/dingdawg-planning-agent
- Socket report: https://socket.dev/npm/package/dingdawg-planning-agent
- Repository: https://github.com/dingdawg/dingdawg-agent-1
- Changelog RSS feed: https://verifymcp.io/servers/dingdawg-dingdawg-planning-agent/dingdawg-planning-agent/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/dingdawg-dingdawg-planning-agent/dingdawg-planning-agent/changelog.json
- HTML version of this page: https://verifymcp.io/servers/dingdawg-dingdawg-planning-agent/dingdawg-planning-agent