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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
scan_piiA

Scan a file or folder for PII and return TYPES + LOCATIONS + COUNTS only — never the detected values. Safe to surface to an LLM: it learns what PII exists and where, without the substance entering context. Recurses into subfolders; skips unreadable files and caps very large sheets, reporting both as notes.

entities is an optional PATH to a local .obsify.entities file (one name per line) of KNOWN names to hide; matches (incl. suffix/abbreviation variants) are reported as KNOWN_ENTITY. If omitted, a nearby .obsify.entities is auto-used. The names are read locally and never returned.

redact_textA

Return text with detected PII replaced by placeholders (e.g. , ). Deterministic; checksum-validated identifiers and context/precision rules apply so bare numbers are not over-masked.

entities is an optional PATH to a local .obsify.entities file of KNOWN names to hide; matches (incl. variants) are masked as . If omitted, a nearby .obsify.entities is auto-used.

verify_value_freeA

Fail-closed check that text contains NONE of terms (nor their suffix-normalized / distinctive-token variants). Returns {"value_free": bool} with zero detail on what matched — for verifying an artifact before it leaves the perimeter.

make_synthetic_twinA

Generate a SYNTHETIC TWIN of a real Excel workbook at path, written to out. Schema (sheets, headers, column types, true row counts) is preserved; every data value is freshly FAKED — no real value is copied. Reason and write your analysis code against the twin; then run it on the real file with run_on_real. Returns the schema summary (safe shape).

run_on_realA

COMPUTE-TO-DATA: execute your Python code LOCALLY against the real file at data_path (bound to the variable DATA_PATH in your code); the returned output is size-capped and best-effort PII-masked. The data never enters your context; substance never leaves. Return AGGREGATES (counts/sums/summaries) via print() — output masking is defense-in-depth, NOT a guarantee (NER can miss a name in a raw record), so never print raw records or identifiers. The masking field carries this caveat with the result. Network is disabled and a timeout applies.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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