io.github.kakarot-dev/deepmiro
NPM · DEEPMIRO-MCP · SCANNED AUG 3
Simulate hundreds of AI agents to predict how communities react to events and policies
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 Security87
- No malware found by supply-chain analysis.Pass
- Only part of the dependency tree could be resolved (108 of 109), so this covers what we could see, not the whole tree.Partial
- No install/post-install scripts declared.Pass
- Only part of the dependency tree could be resolved (108 of 109), so this covers what we could see, not the whole tree. View diagnostics → Partial
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 (AGPL-3.0-only).Pass
- Actively maintained (last published 106 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability85
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (excellent).Pass
- Tool/resource definitions use about 917 tokens (~91/item across 10 items; 9 tools + 1 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management23
- Stability observed for 7 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
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
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.
npm · deepmiro-mcp
claude mcp add kakarot-dev-deepmiro -- npx -y deepmiro-mcp
codex mcp add kakarot-dev-deepmiro -- npx -y deepmiro-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"kakarot-dev-deepmiro": {
"type": "local",
"command": [
"npx",
"-y",
"deepmiro-mcp"
],
"enabled": true
}
}
} openclaw mcp add kakarot-dev-deepmiro --command npx --arg -y --arg deepmiro-mcp
mcp_servers:
kakarot-dev-deepmiro:
command: "npx"
args: ["-y", "deepmiro-mcp"] {
"mcpServers": {
"kakarot-dev-deepmiro": {
"command": "npx",
"args": [
"-y",
"deepmiro-mcp"
]
}
}
} 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.
- 3 Aug 26 +4
- Stability: unverified → 0.23 ▲ functional
- 2 Aug 26 +41
- Provenance: unverified → fail ▼ security
- Known CVEs: unverified → partial ▲ security
- Install scripts: unverified → pass ▲ security
- Malware scan: unverified → pass ▲ security
- Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window). security
- Security disclosure: fail → unverified ▼ functional
- Tool coverage: 100 → unverified ▼ functional
- Schema quality: 100 → unverified ▼ functional
- Schema quality: unverified → excellent ▲ functional
- License: unverified → pass ▲ functional
- Dependency health: unverified → partial ▲ functional
- Maintenance: unverified → pass ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Licence: AGPL-3.0-only functional
- 31 Jul 26 −9
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 30 Jul 26 −18
- Malware scan: pass → unverified ▼ security
- 27 Jul 26 53
First indexed and scored.
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 npm/[email protected]
Provenance none
Ecosystem: npm · Outcome: none
Dependencies 108 packages
108 packages in the resolved dependency tree · 108 deprecated · 32 stale.
The dependency tree was only partially resolved, so these counts may be incomplete.
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.
create_simulation Create Simulation ~171
Run a full swarm prediction. Builds a knowledge graph, generates agent personas, runs a multi-agent social media simulation, and generates a prediction report. Streams progress updates. Returns the final report when complete.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_count | integer | — | Override agent count |
| document_id | string | — | ID of a pre-uploaded document (from upload_document tool). Skips file upload and uses server-side sanitized text. |
| platform | string | — | Target platform(s). Default: both |
| preset | string | — | Simulation preset: quick (10 agents, 20 rounds), standard (20/40), deep (50/72) |
| prompt | string | yes | Scenario description. E.g. 'How will crypto twitter react to a new ETH ETF rejection?' |
| rounds | integer | — | Override simulation rounds |
No output schema declared.
No examples provided.
get_report Get Report ~66
Generate and retrieve the prediction report for a completed simulation. If the report hasn't been generated yet, triggers generation (may take 1-3 minutes). Returns a detailed markdown analysis of the simulation results.
| Name | Type | Req | Description |
|---|---|---|---|
| simulation_id | string | yes | The simulation ID to generate/fetch a report for |
No output schema declared.
No examples provided.
interview_agent Interview Agent ~97
Chat with a specific simulated agent to understand their perspective, reasoning, and predicted behavior. The agent responds in character based on their persona and simulation experience.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_id | integer | yes | The agent's numeric ID within the simulation |
| message | string | yes | Question or prompt to send to the agent |
| platform | string | — | Which platform persona to interview. Omit for both. |
| simulation_id | string | yes | The simulation ID |
No output schema declared.
No examples provided.
list_simulations List Simulations ~34
List past simulation runs with their status and metadata.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | — | Max results to return (default 20) |
No output schema declared.
No examples provided.
quick_predict Quick Predict ~74
Fast, lightweight prediction without running a full simulation. Uses the LLM to simulate swarm behavior and predict outcomes. Returns in seconds. For deeper analysis, use create_simulation instead.
| Name | Type | Req | Description |
|---|---|---|---|
| prompt | string | yes | Scenario to predict. E.g. 'How will the public react if Apple announces a $2000 iPhone?' |
No output schema declared.
No examples provided.
search_simulations Search Simulations ~42
Search past simulations by topic, project name, or simulation ID.
| Name | Type | Req | Description |
|---|---|---|---|
| query | string | yes | Search term — matches against simulation ID, project name, or requirement |
No output schema declared.
No examples provided.
simulation_data Simulation Data ~219
Access simulation data: agent profiles, configuration, action logs, social media posts, round-by-round timeline, per-agent activity stats, and interview history. Paginated — use offset to get more results when has_more is true.
| Name | Type | Req | Description |
|---|---|---|---|
| action_type | string | — | Filter actions by type (CREATE_POST, LIKE_POST, etc.) |
| agent_name | string | — | Filter actions by agent name |
| data_type | string | yes | What data to retrieve: overview (condensed summary: entities, agents, graph, config, action stats — start here), profiles (full agent personas), config (simulation parameters), actions (agent action… |
| limit | integer | — | Max results per page (default 50) |
| offset | integer | — | Offset for pagination (default 0) |
| platform | string | — | Filter by platform (for actions and posts) |
| simulation_id | string | yes | The simulation ID |
No output schema declared.
No examples provided.
simulation_status Simulation Status ~75
Check the progress of a running or completed simulation. Returns phase-aware status with entity names and action content. Phases: building_graph → generating_profiles → simulating → completed.
| Name | Type | Req | Description |
|---|---|---|---|
| detailed | boolean | — | Include recent agent actions with content in the response |
| simulation_id | string | yes | The simulation ID returned by create_simulation |
No output schema declared.
No examples provided.
upload_document Upload Document ~118
Upload a document for use in simulations. LIMITS: Max 10MB, PDF/MD/TXT only. The server extracts text server-side (PyMuPDF for PDFs). Returns a document_id to pass to create_simulation. NOTE: Only works with local file paths (stdio transport). For remote/hosted mode, the client skill uploads via HTTP instead.
| Name | Type | Req | Description |
|---|---|---|---|
| file_path | string | yes | Absolute path to the file to upload. Supported: PDF, MD, TXT. Max 10MB. Rejects binary files and unsupported formats. |
No output schema declared.
No examples provided.