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Calibrated numeric estimates with uncertainty from messy records
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Calibrated numeric estimates with uncertainty from messy records
Security Report
Valid MCP server (1 strong, 0 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
3 files analyzed · 1 issue found
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This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: MAAP_API_KEY
Environment variable: MAAP_BASE_URL
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-codecraf8-maap": {
"env": {
"MAAP_API_KEY": "your-maap-api-key-here",
"MAAP_BASE_URL": "your-maap-base-url-here"
},
"args": [
"-y",
"maap-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
maap-mcp
maap over MCP: ask for a number, get a calibrated distribution — a median, the range the true value should fall inside, and the probability of crossing a threshold you name.
Two tools:
| Tool | Does |
|---|---|
list_targets | Search the 104 trained targets. Call this first — a target id is not guessable |
estimate | One case, one target → median, quantiles, intervals, P(value > t), and how calibrated the answer is |
Install
Get a key at dash.hitl.systems/keys, then add the server to your client.
Claude Desktop — claude_desktop_config.json:
{
"mcpServers": {
"maap": {
"command": "npx",
"args": ["-y", "maap-mcp"],
"env": { "MAAP_API_KEY": "apikey_..." }
}
}
}
Claude Code:
claude mcp add maap --env MAAP_API_KEY=apikey_... -- npx -y maap-mcp
Not on npm yet — until it is, install straight from this repo by swapping maap-mcp for
github:hitl-systems/maap-mcp in any of the above. The built bundle is committed, so there is
nothing to compile.
Cursor — ~/.cursor/mcp.json: the same mcpServers block as Claude Desktop.
| Variable | Required | Default |
|---|---|---|
MAAP_API_KEY | yes | — |
MAAP_BASE_URL | no | https://dash.hitl.systems |
Using it
Which maap target predicts flight prices? Estimate the fare for a Vistara economy ticket Delhi → Mumbai, non-stop, booked 9 days out, and tell me the chance it is over ₹10,000.
The agent calls list_targets for the id, then estimate with your case as the state.
Read the median as the answer and the range as what to plan against. For a threshold
question, pass thresholds and read P(value > t) — do not compare the median to the
threshold yourself; the median says where the middle is, not how likely the threshold is.
An answer also carries how much to trust it: regime (in_domain, or new_target when the
model only knows what your examples taught it), calibration ("none" means no correction
survived its check, so the range carries no coverage promise), and novelty_flag when the case
is unlike anything in training. Known limits are published:
dash.hitl.systems/docs/calibration.
Notes
- One question per call. The HTTP API takes up to 32 under names you choose; a flat tool is the one an agent fills in correctly.
- The first call after an idle period starts a GPU container and can take tens of seconds. The server waits 120 s and tells the agent to retry rather than failing silently.
- Each question counts against your allowance. A rejected call costs nothing.
- Pricing, limits and the full wire format: dash.hitl.systems/docs/api.
MIT.
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