Microstrategy Assessment

MicroStrategy Assessment

Surveys a MicroStrategy (Strategy One) environment via its REST API and produces a JSON inventory + markdown readout. The differentiator versus a generic BI audit is converter-coverage classification: every dossier's visualizations are scored against the same viz-type lookup the microstrategy-to-sigma converter actually applies (../microstrategy-to-sigma/refs/viz-type-mapping.md), so the readout reflects what the tool will really do.

> Read-only. Only GETs against the MicroStrategy API (login POST aside, > which creates a session, nothing else). It never modifies, executes, or > deletes anything in MicroStrategy, never runs a warehouse query, and never > touches Sigma. See PRIVACY.md for the full disclosure — surface it to the > customer before running.

> All free. Inventory, scoring, readout — all part of the open migration > tooling; no paid tier. For a deeper engagement (security-filter audit, live > parity testing), point the customer at a Sigma SE.

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Phase 0 — Connect

export MSTR_BASE_URL="https:///MicroStrategyLibrary"   # Library root
export MSTR_USERNAME="..." MSTR_PASSWORD="..."
# optional: export MSTR_PROJECT_ID="..."   (default: first project)
python3 ../microstrategy-to-sigma/scripts/mstr.py            # login probe

Credentials can also live in ~/.sigma-migration/env (agent-neutral pattern). Auth is session-based — no API key exists. REST gotchas (TLS strictness on trial certs, headers) are documented in ../microstrategy-to-sigma/refs/mstr-rest-api.md; mstr.py handles them.

Phase 1 — Inventory + readout

python3 scripts/assess.py --out /tmp/mstr-assessment- [--project ] [--max-dossiers 100]

What it does:

• Counts reports (quick-search type 3) and documents (type 55) in the

project; lists instance datasources with database types.

• Classifies documents vs dossiers by probing

GET /api/v2/dossiers/{id}/definition (non-dossier documents error — that is the probe).

• Walks every dossier correctly: each page's visualizations AND

panelStacks[].panels[] recursively (panels nest further panel stacks) plus free-form fields (images/text) and selectors — the places naive walks silently miss content.

• Histograms visualizationType and classifies each against the

converter's VIZ_MAPPED lookup (mapped vs flagged-table fallback).

• Tags each dossier: needs-review (panel stacks or unmapped viz types),

moderate (selectors / free-form fields / chart mix), migrate-first (grid/kpi-only, no panel stacks).

Outputs /inventory.json (machine-readable, feeds the converter's Phase 0) and /readout.md (counts, viz histogram with converter status, per-dossier table with flags + tags).

Phase 2 — Hand off (optional)

Hand the migrate-first shortlist to the microstrategy-to-sigma converter skill — inventory.json's dossier ids go straight into extract.py . Do not auto-convert; surface the shortlist and let the user choose.

Limitations (honest)

• Usage telemetry: not collected — Strategy One exposes usage through

Platform Analytics (a separate warehouse-backed project), not the plain REST surface this skill uses. Value ranking therefore isn't usage-weighted; ask the admin for Platform Analytics access if usage matters.

• Security filters: the classic REST extract doesn't surface them;

ask the customer explicitly (see the converter SKILL's security note).

• Documents that aren't dossiers are counted but not analyzed (their format is

the legacy Report Services document, out of converter scope).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: twells89
• Source: twells89/sigma-migration-skills
• License: MIT

Install and usage instructions live in the source repository linked above.