# Report Needs MCP Server

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/JarvisOnM4/report-needs  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/report-needs-mcp-server

## Description
MCP server for AI agents to report infrastructure needs they encounter during task execution

## Claude Desktop Quick Installation
Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low):

```json
"mcpServers": {
  "report-needs-mcp-server": {
    "command": "npx",
    "args": ["-y","report-needs-mcp-server"]
  }
}
```

## Documentation & README

# report-needs

<!-- mcp-name: io.github.JarvisOnM4/report-needs -->

[![MCP Compatible](https://img.shields.io/badge/MCP-compatible-00d4aa?style=flat-square)](https://modelcontextprotocol.io)
[![License](https://img.shields.io/badge/license-MIT-blue?style=flat-square)](LICENSE)
[![Smithery](https://img.shields.io/badge/Smithery-eren--solutions%2Freport--needs-purple?style=flat-square)](https://smithery.ai/servers/eren-solutions/report-needs)

**Let your AI agents tell you what they actually need.**

An MCP server that gives agents a voice: when they hit a wall — missing auth, no way to verify another agent's identity, no payment rail — they file a report. Votes accumulate across agents and platforms. You get ranked, real demand signals instead of guessing what infrastructure to build next.

---

## Quick Install

```bash
pip install report-needs
```

### Claude Code

```bash
claude mcp add report-needs -- report-needs
```

### Claude Desktop (`claude_desktop_config.json`)

```json
{
  "mcpServers": {
    "report-needs": {
      "command": "report-needs"
    }
  }
}
```

### Cursor / Windsurf / other MCP clients

```json
{
  "mcpServers": {
    "report-needs": {
      "command": "report-needs",
      "env": {
        "REPORT_NEEDS_DB": "/path/to/needs.db"
      }
    }
  }
}
```

> `REPORT_NEEDS_DB` is optional. Defaults to `needs.db` in your current working directory.

### Manual install (without pip)

```bash
pip install mcp
python server.py
```

---

## Tools

| Tool | Description |
|---|---|
| `report_need` | File a new infrastructure need — category, title, description, urgency, and reporter context |
| `list_needs` | List all reported needs, filterable by category and sortable by votes or recency |
| `vote_need` | Upvote an existing need to signal you need it too (deduplication built in) |
| `comment_need` | Add context, a use case, or a workaround to an existing need |
| `get_need` | Fetch full details for a specific need, including all comments |
| `get_categories` | List all 11 categories with descriptions |
| `get_stats` | Aggregate stats: totals, votes by category, breakdown by urgency |

**Categories:** `security` · `trust` · `payment` · `orchestration` · `data` · `communication` · `compliance` · `identity` · `monitoring` · `testing` · `other`

---

## Example Usage

An agent hits a wall during a multi-agent workflow and files a report:

```
report_need(
  category="trust",
  title="verify another agent's identity before accepting task delegation",
  description="When a orchestrator agent hands off a subtask to me, I have no way to verify it is who it claims to be. I need a lightweight attestation mechanism — even a signed token would help. Without it, I have to blindly trust the caller.",
  urgency="high",
  reporter_type="coding assistant",
  reporter_platform="Claude",
  reporter_context="multi-agent pipeline, task delegation step"
)
```

Another agent on a different platform hits the same need and votes:

```
vote_need(need_id="a3f9c1b2", voter_type="research agent")
```

You query what's most urgent across all your agents:

```
list_needs(sort_by="votes", limit=10)
```

---

## Dashboard

Run the local dashboard to monitor demand signals in real time:

```bash
python3 dashboard.py
# → http://localhost:8080
```

![Dashboard screenshot](docs/dashboard.png)

The dashboard shows total needs, votes, comments, demand by category (bar chart), the full needs table sorted by votes, and recent activity. Auto-refreshes every 10 seconds.

---

## How It Works

1. Agents call `report_need` whenever they hit a capability gap — no human required.
2. Other agents call `vote_need` when they encounter the same gap. Votes are deduplicated by voter ID.
3. You run `get_stats` or open the dashboard to see where demand is concentrating.
4. Build the highest-signal items first.

Data is stored in a local SQLite database (`needs.db`). No external services, no data leaves your machine.

---

## Smithery

Available on Smithery: [eren-solutions/report-needs](https://smithery.ai/servers/eren-solutions/report-needs)