io.github.Roberton003/mcp-server-decisions
PYPI · MCP-SERVER-DECISIONS · SCANNED AUG 3
Decision tracking with prediction validation and outcome gates for AI agents
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 →
Supply Chain Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency45
- 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 11 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability0
- Schema quality not yet verified: our sandbox run of this package did not complete, so we have no schema to assess.Unverified
Stability & Change Management0
- Stability not yet verified: our sandbox run of this package did not complete, so we have no schema to compare.Unverified
Tool Coverage0
- Tool coverage not yet verified: our sandbox run of this package did not complete, so we have no tool definitions to assess.Unverified
Capabilities0
- Protocol version not yet verified: our sandbox run of this package did not complete, so we never saw its MCP handshake.Unverified
Unverified: 4 categories
Categories scored 0 because our sandbox run of this package has not given us the schema these checks need to read. That is a gap on our side rather than a finding about the package, and we only credit what we can confirm, so the score stands at 0 until the capture succeeds. We are working through the fleet, so this normally clears without any action from you. How we score packages →
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
pypi · mcp-server-decisions
claude mcp add roberton003-mcp-server-decisions -- uvx mcp-server-decisions
codex mcp add roberton003-mcp-server-decisions -- uvx mcp-server-decisions
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"roberton003-mcp-server-decisions": {
"type": "local",
"command": [
"uvx",
"mcp-server-decisions"
],
"enabled": true
}
}
} openclaw mcp add roberton003-mcp-server-decisions --command uvx --arg mcp-server-decisions
mcp_servers:
roberton003-mcp-server-decisions:
command: "uvx"
args: ["mcp-server-decisions"] {
"mcpServers": {
"roberton003-mcp-server-decisions": {
"command": "uvx",
"args": [
"mcp-server-decisions"
]
}
}
} 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.
- 1 Aug 26 −5
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 31 Jul 26 −18
- Malware scan: pass → unverified ▼ security
- 28 Jul 26 0
- Tool coverage: Tool coverage not yet verified: our sandbox run of this package did not complete, so we have no tool definitions to assess. functional
- Schema quality: Schema quality not yet verified: our sandbox run of this package did not complete, so we have no schema to assess. functional
- 26 Jul 26 47
First indexed and scored.
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
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 3 Aug 2026 · Analysed pypi/[email protected]
Provenance none
Ecosystem: pypi · Outcome: none
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Dependencies 0 packages
0 packages in the resolved dependency tree.
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.
query-decisions ~77
Search prior decisions by keyword, technology, or domain
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | — | Partial domain filter |
| keyword | string | — | Search in problem + solution + alternatives (case-insensitive) |
| max_results | integer | — | Maximum results to return |
| technology | string | — | Exact technology filter (e.g., 'duckdb') |
No output schema declared.
No examples provided.
record-decision ~165
Register a technical decision with problem, solution, alternatives, and technologies
| Name | Type | Req | Description |
|---|---|---|---|
| adr_ref | string | — | Reference to an associated ADR, if any (e.g., 'docs/adr/adr-012-duckdb.md') |
| chosen_solution | string | yes | The solution that was chosen |
| domain | string | — | Technical domain (e.g., 'data-engineering', 'backend', 'ai') |
| predictions | array | — | Initial predictions known at decision time (optional) |
| problem | string | yes | The problem or technical opportunity identified |
| rejected_alternatives | array | yes | Alternatives considered and rejected |
| status | string | — | Decision status |
| technologies | array | yes | Technologies involved in this decision (e.g., ['duckdb', 'parquet']) |
No output schema declared.
No examples provided.
record-outcome ~110
Close the prediction loop: record actual results and calculate accuracy
| Name | Type | Req | Description |
|---|---|---|---|
| accuracy_score | integer | yes | Accuracy of prediction (0-100). >=90 excellent, 50-89 acceptable, <50 failed |
| actual_value | string | yes | Observed result (e.g., 'latency p99 = 180ms') |
| measurement_source | string | yes | Where the measurement came from |
| prediction_id | string | yes | Prediction ID (e.g., 'PRD-2026-0001') |
No output schema declared.
No examples provided.
record-prediction ~82
Register a measurable prediction linked to a decision
| Name | Type | Req | Description |
|---|---|---|---|
| decision_id | string | yes | Decision ID (e.g., 'DEC-2026-0001') |
| predicted_value | string | yes | Expected result (e.g., 'latency p99 < 200ms', 'cost < $100/month') |
| prediction_type | string | yes | Type of prediction |
No output schema declared.
No examples provided.