io.github.dingdawg/dingdawg-planning-agent
NPM · DINGDAWG-PLANNING-AGENT · SCANNED SEP 21
Planning AI. Sprint estimation, task breakdown, risk analysis. Learns your velocity.
Available components
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. How we score → Why this is hard to score →
Supply Chain Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 31 of 96 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency48
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 76 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability75
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 425 tokens (~70/item across 6 items; 6 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management93
- Stability observed for 28 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the io.github.dingdawg/dingdawg-planning-agent MCP server?
io.github.dingdawg/dingdawg-planning-agent runs locally as an npm package, launched with npx -y dingdawg-planning-agent. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · dingdawg-planning-agent
claude mcp add dingdawg-dingdawg-planning-agent -- npx -y dingdawg-planning-agent
{
"mcpServers": {
"dingdawg-dingdawg-planning-agent": {
"command": "npx",
"args": [
"-y",
"dingdawg-planning-agent"
]
}
}
} {
"servers": {
"dingdawg-dingdawg-planning-agent": {
"command": "npx",
"args": [
"-y",
"dingdawg-planning-agent"
]
}
}
} codex mcp add dingdawg-dingdawg-planning-agent -- npx -y dingdawg-planning-agent
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"dingdawg-dingdawg-planning-agent": {
"type": "local",
"command": [
"npx",
"-y",
"dingdawg-planning-agent"
],
"enabled": true
}
}
} openclaw mcp add dingdawg-dingdawg-planning-agent --command npx --arg -y --arg dingdawg-planning-agent
mcp_servers:
dingdawg-dingdawg-planning-agent:
command: "npx"
args: ["-y", "dingdawg-planning-agent"] {
"McpServers": {
"dingdawg-dingdawg-planning-agent": {
"Transport": "stdio",
"Command": "npx",
"Arguments": [
"-y",
"dingdawg-planning-agent"
]
}
}
} assistant mcp add dingdawg-dingdawg-planning-agent -t stdio -c npx -a -y dingdawg-planning-agent
{
"mcpServers": {
"dingdawg-dingdawg-planning-agent": {
"command": "npx",
"args": [
"-y",
"dingdawg-planning-agent"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 21 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 19 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 17 Sept 26 −3
- Stability: pass → 0.80 functional
- 16 Sept 26 +1
- Stability: 0.97 → pass security
- 14 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 12 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 10 Sept 26 −3
- Stability: pass → 0.80 functional
- 9 Sept 26 +1
- Stability: 0.97 → pass security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 21 Sept 2026 · Analysed npm/dingdawg-planning-agent@2.0.6
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Dependencies 96 packages
| Packages resolved | 96 |
|---|---|
| Stale | 31 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
decision_matrix ~69
AI-powered structured decision making with weighted scoring, bias detection, and assumption tracking. Requires DINGDAWG_API_KEY.
| Name | Type | Req | Description |
|---|---|---|---|
| criteria | string | – | Evaluation criteria (comma-separated) |
| decision | string | yes | Decision to evaluate |
| options | string | yes | Options to compare (comma-separated or described) |
No output schema declared.
No examples provided.
estimate_effort ~73
Free AI effort estimation with complexity scoring and PERT ranges. Deep LLM-powered subtask breakdown and confidence analysis with API key.
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | – | Project context, tech stack, constraints |
| task | string | yes | Task or feature to estimate |
| team_experience | string | – | Team experience level |
No output schema declared.
No examples provided.
plan_project ~82
Generate a structured project plan with phases, milestones, dependencies, and resource allocation. Requires DINGDAWG_API_KEY for LLM-powered planning.
| Name | Type | Req | Description |
|---|---|---|---|
| deadline | string | – | Target deadline (YYYY-MM-DD) |
| methodology | string | – | Project methodology |
| project_description | string | yes | Describe the project to plan |
| team_size | number | – | Team size |
No output schema declared.
No examples provided.
retrospective ~80
AI project retrospective — what went well, what didn't, action items, pattern identification. Requires DINGDAWG_API_KEY.
| Name | Type | Req | Description |
|---|---|---|---|
| project | string | yes | Project name or description |
| team_feedback | string | – | Team feedback or notes |
| went_poorly | string | – | What went poorly |
| went_well | string | – | What went well |
No output schema declared.
No examples provided.
risk_analyze ~61
Free AI project risk analysis with risk matrix scoring. Deep LLM-powered mitigation strategies and contingency plans with API key.
| Name | Type | Req | Description |
|---|---|---|---|
| known_risks | string | – | Known risks or concerns |
| project_description | string | yes | Project or initiative to analyze for risks |
No output schema declared.
No examples provided.
standup_summary ~60
AI standup summary from team updates — blocker detection, dependency alerts, velocity tracking. Requires DINGDAWG_API_KEY.
| Name | Type | Req | Description |
|---|---|---|---|
| sprint_goal | string | – | Current sprint goal |
| updates | string | yes | Team standup updates (paste all updates) |
No output schema declared.
No examples provided.
What is the io.github.dingdawg/dingdawg-planning-agent MCP server?
io.github.dingdawg/dingdawg-planning-agent is an MCP server listed in the public MCP registry as io.github.dingdawg/dingdawg-planning-agent. Planning AI. Sprint estimation, task breakdown, risk analysis. Learns your velocity. This page covers its npm package (dingdawg-planning-agent).
Is the io.github.dingdawg/dingdawg-planning-agent MCP server safe to use?
io.github.dingdawg/dingdawg-planning-agent scores 84 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 21 September 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.dingdawg/dingdawg-planning-agent MCP server expose?
io.github.dingdawg/dingdawg-planning-agent exposes 6 tools: plan_project, estimate_effort, risk_analyze, decision_matrix, retrospective, standup_summary. Their descriptions and schemas cost roughly 425 tokens of context every time the server is loaded.
Is the io.github.dingdawg/dingdawg-planning-agent MCP server still maintained?
io.github.dingdawg/dingdawg-planning-agent is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.dingdawg/dingdawg-planning-agent MCP server under?
io.github.dingdawg/dingdawg-planning-agent declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.