Skip to main content
Glama

sphere-mcp

The SPHERE privacy boundary, exposed as an MCP server. An agent analyses sensitive data without ever receiving an individual record.

Status: Phase 1. The loop works end to end. The SDC gate and the disclosure ladder are not built yet — see Limits below, and do not make claims beyond them.

What it does

Tool

The model receives

sphere_open(path)

A format-redacted profile: names, dtypes, distinct counts, missingness, masked patterns (9/X). No cell values.

sphere_twin(seed?)

A certified synthetic twin + fidelity/privacy scores. Safe to read.

sphere_run(code)

Full results — the twin is synthetic.

sphere_deploy(code)

Only whether it worked. Results go to the user, on disk.

sphere_simulate(code, perturbation)

Debugs a real-data discrepancy by perturbing the twin until it reproduces the symptom.

sphere_status()

An audit ledger of everything that crossed into the model's context.

Related MCP server: agami-core

Setup (Claude Desktop)

~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "sphere": {
      "command": "/opt/homebrew/bin/node",
      "args": ["/absolute/path/to/sphere-mcp/src/server.js"],
      "env": {
        "SPHERE_CLI": "/absolute/path/to/sphere-cli/bin/sphere.js",
        "SPHERE_VAULT": "~/.sphere/vault"
      }
    }
  }
}

Restart Claude Desktop. Then:

I have sensitive data at /path/to/patients.csv. Use SPHERE — I don't want you reading the real records. Profile it, make a twin, explore what's there, and when the analysis looks right, deploy it to the real data.

Results from the real run land in the session directory as real-output.txt and real-*.png. You read them; the model does not.

How the guarantee is enforced

Not by asking the model nicely. Two mechanisms:

  1. What we send — the profiler emits shapes, never values. The real path is never returned to the model (sphere_status says so explicitly).

  2. What model-authored code can reach — a macOS Seatbelt profile that denies the whole home tree and re-allows exactly the twin plus a session directory, with network denied outright and an allow-list environment carrying no credentials.

Verified by attacking it (node test/boundary.test.mjs):

ok  blocked  read the real CSV by absolute path
ok  blocked  read the real CSV via pandas
ok  blocked  list the home directory for other datasets
ok  blocked  open a network socket to exfiltrate
ok  blocked  harvest credentials from the environment
ok  blocked  write outside the session directory
ok  blocked  read a dataset OUTSIDE the home directory
ok  ALLOWED  read the twin (this is the point)

That second-to-last case is there because the first two versions of the sandbox failed it. Denying $HOME misses data on /tmp, /Volumes or a lab mount; and once denied, /tmp still resolved to /private/tmp, which Seatbelt matches instead — so the rule looked right and never fired. Both forms of every path are emitted now.

Limits — read before claiming anything

  • No SDC gate yet. sphere_deploy withholds results from the model entirely, so nothing real flows back today. The moment you add a return path, aggregate outputs can disclose individuals (n=1 group means, min/max, residuals, a scatter plot). Build the gate before you open that door.

  • A twin is not automatically non-disclosive. Extreme values in particular deserve checking before you treat a twin as safe to share — a maximum is always some real individual's value, wherever it appears. Evaluate each twin on its own evidence rather than assuming the category is safe.

  • The deny list is a list. The registered dataset is always denied, by resolved path and containing directory, along with the usual data locations ($HOME, /Users, /Volumes, /mnt, /media, /srv, /data, /tmp). A file outside all of those is readable. Denying everything and allow-listing instead is the better shape and was tried: (deny file-read*) also denies metadata, so the loader cannot resolve its own binary and Python dies with SIGABRT before main(). Worth revisiting with a pinned interpreter.

  • macOS only. The sandbox is Seatbelt. Linux needs bubblewrap or a container, and Linux is what any institutional deployment will run.

  • Other tools bypass everything. This server controls its own tools. If the same client also has shell or filesystem access to the real path, the boundary is a convention. In Claude Desktop that is usually fine (no filesystem tool by default). In Claude Code, add a deny rule; at institutional scale, put the data under a different OS user or in a container.

  • Column names reach the model. Alias them if a name is itself sensitive.

Testing

node test/boundary.test.mjs

The tests attack the server rather than exercising it, and each case declares what "blocked" looks like for that mechanism — a denied read raises, a denied directory lists empty, a scrubbed environment yields no keys. Do not collapse these into "did it error?"; an earlier version did and reported two false leaks. A traceback also echoes the offending source line, so never assert on a success sentinel that appears in the code itself.

F
license - not found
-
quality - not tested
C
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • F
    license
    -
    quality
    C
    maintenance
    A governed MCP server for integrating AI agents with customer data, featuring role-based access control, field redaction, and human-in-the-loop approval for secure support operations.
    1
  • F
    license
    -
    quality
    B
    maintenance
    A governed MCP server that enforces a trust layer between AI agents and databases, requiring sign-off on joins and metrics and producing auditable receipts for every query.
  • A
    license
    A
    quality
    B
    maintenance
    MCP server providing on-prem PII detection and anonymization tools (scan and is_sensitive) for AI agents, ensuring data stays local.
    4
    MIT
  • A
    license
    -
    quality
    C
    maintenance
    A reference MCP server demonstrating safe agent access to multi-tenant CRM data with tenant isolation enforced in the data layer, role-based permissions, and human confirmation on writes.
    MIT

View all related MCP servers

Related MCP Connectors

  • MCP server teaching AI agents to implement TideCloak: auth, E2EE, IGA, security analysis

  • MCP server connecting AI agents to non-custodial staking data across 130+ networks.

  • An MCP server for deep research or task groups

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/statzihuai/sphere-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server