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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
GEMINI_MODELNoModel name to use for Gemini review. Defaults to 'gemini-2.5-flash'.gemini-2.5-flash
MCP_TRANSPORTNoTransport for the MCP server, e.g., 'streamable-http' for remote transport. If not set, the default transport is used.
GEMINI_API_KEYNoAPI key for the optional Gemini semantic review feature.
LEAKAGELENS_DATA_ROOTYesThe root directory that all file tools are restricted to. Must be an absolute path.

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
profile_datasetA

Profile a local CSV, Parquet, or JSONL without sending the full dataset to the model.

audit_datasetC

Detect target, entity and temporal leakage, PII, identifiers, and metric risks.

audit_training_codeA

Statically detect preprocessing or resampling before the train/test split.

review_feature_availabilityC

Optionally ask Gemini which features may be unavailable at the prediction moment.

Prompts

Interactive templates invoked by user choice

NameDescription
review_before_trainingCreate a disciplined pre-training review workflow.

Resources

Contextual data attached and managed by the client

NameDescription
experiment_contractQuestions that define a valid supervised-learning experiment.

TDQS

B3.3/5.0

Scored across 4 tools

Disambiguation4/5

Each tool targets a distinct stage of leakage detection: profiling, dataset auditing, code auditing, and feature availability review. However, profile_dataset and audit_dataset could be confused by an agent since both operate on datasets, though their descriptions help separate general profiling from leakage-specific auditing.

Naming Consistency5/5

All tool names consistently follow a verb_noun pattern using lowercase snake_case: profile_dataset, audit_dataset, audit_training_code, and review_feature_availability. This makes the tool set predictable and easy to navigate.

Tool Count5/5

Four tools is well-scoped for a specialized leakage-detection server. Each tool has a clear, non-redundant role, and the count feels appropriate rather than thin or bloated.

Completeness4/5

The tool surface covers the core leakage-detection workflow: profile data, audit data for leakage, audit training code, and verify feature availability. Minor gaps exist around remediation or actionable reporting after an audit, but the main detection loop is complete.

Maintenance

ActivitySlowing
ResponsivenessNo issues