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MCP server for AI agents to report infrastructure needs they encounter during task execution
MCP server for AI agents to report infrastructure needs they encounter during task execution
Valid MCP server (1 strong, 7 medium validity signals). 3 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
7 files analyzed · 4 issues found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-jarvisonm4-report-needs": {
"args": [
"report-needs"
],
"command": "uvx"
}
}
}From the project's GitHub README.
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.
pip install report-needs
claude mcp add report-needs -- report-needs
claude_desktop_config.json){
"mcpServers": {
"report-needs": {
"command": "report-needs"
}
}
}
{
"mcpServers": {
"report-needs": {
"command": "report-needs",
"env": {
"REPORT_NEEDS_DB": "/path/to/needs.db"
}
}
}
}
REPORT_NEEDS_DBis optional. Defaults toneeds.dbin your current working directory.
pip install mcp
python server.py
| 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
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)
Run the local dashboard to monitor demand signals in real time:
python3 dashboard.py
# → http://localhost:8080

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.
report_need whenever they hit a capability gap — no human required.vote_need when they encounter the same gap. Votes are deduplicated by voter ID.get_stats or open the dashboard to see where demand is concentrating.Data is stored in a local SQLite database (needs.db). No external services, no data leaves your machine.
Available on Smithery: eren-solutions/report-needs
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