io.github.kakarot-dev/deepmiro
NPM · DEEPMIRO-MCP
Simulate hundreds of AI agents to predict how communities react to events and policies
Available components
Last scanned (36 minutes ago)
Scores higher than 68% of npm servers
Category AI and Machine Learning
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 Security81
- No malware found by supply-chain analysis.Pass
- CVE check failed: a known high-severity CVE affects zod 3.25.76, a direct dependency. A fixed version is available. View diagnostics → Fail
- No install/post-install scripts declared.Pass
- 33 of 106 dependencies flagged as unhealthy. 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 200 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability87
- 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 Management100
- No destabilizing schema changes in the last 30 days.Pass
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 9 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 10 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
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.
- 4 Nov 26 0
- Stability: 0.97 → pass security
- 3 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
- 2 Nov 26 −1
- Stability: pass → 0.93 functional
- 31 Oct 26 0
- Security disclosure: unverified → fail ▼ functional
- 30 Oct 26 0
- Security disclosure: fail → unverified ▼ functional
- 27 Oct 26 0
- Stability: 0.97 → pass security
- 26 Oct 26 0
- Stability: pass → 0.97 functional
- 24 Oct 26 −5
- CVE-2023-54404 affects this package: high ▼ security
- Known CVEs: pass → fail ▼ 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 5 Nov 2026 · Analysed npm/deepmiro-mcp@0.1.2
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 →
Vulnerabilities 1 finding
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-mrhx-c7xc-m3w5 | CVE-2023-54404 | high | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H | yes |
Background: What a vulnerability scan can and cannot prove →
Dependencies 106 packages
| Packages resolved | 106 |
|---|---|
| Stale | 33 |
| 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 →
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.
How do I install the io.github.kakarot-dev/deepmiro MCP server?
io.github.kakarot-dev/deepmiro runs locally as an npm package, launched with npx -y deepmiro-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · deepmiro-mcp
claude mcp add kakarot-dev-deepmiro -- npx -y deepmiro-mcp
What is the io.github.kakarot-dev/deepmiro MCP server?
io.github.kakarot-dev/deepmiro is an MCP server listed in the public MCP registry as io.github.kakarot-dev/deepmiro. Simulate hundreds of AI agents to predict how communities react to events and policies. This page covers its npm package (deepmiro-mcp).
Is the io.github.kakarot-dev/deepmiro MCP server safe to use?
io.github.kakarot-dev/deepmiro scores 81 out of 100 on VerifyMCP. We recorded 1 known advisory against it as of 5 November 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.kakarot-dev/deepmiro MCP server expose?
io.github.kakarot-dev/deepmiro exposes 9 tools: create_simulation, simulation_status, get_report, interview_agent, list_simulations, and 4 more. Their descriptions and schemas cost roughly 896 tokens of context every time the server is loaded.
Is the io.github.kakarot-dev/deepmiro MCP server still maintained?
io.github.kakarot-dev/deepmiro is still listed as active in the MCP registry. We last reached this channel on 5 November 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.kakarot-dev/deepmiro MCP server under?
io.github.kakarot-dev/deepmiro declares the AGPL-3.0-only licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.