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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
GSEP_PRESETNoPreset: minimal, standard, conscious, fullfull
OLLAMA_HOSTNoOllama server URLhttp://localhost:11434
GSEP_HTTP_HOSTNoHTTP server host0.0.0.0
GSEP_HTTP_PORTNoHTTP server port3100
GSEP_LOG_LEVELNosilent, info, debuginfo
GSEP_TRANSPORTNostdio or httpstdio
OPENAI_API_KEYNoOpenAI API key
GSEP_MAX_GENOMESNoMaximum cached genomes before LRU eviction100
ANTHROPIC_API_KEYNoAnthropic API key
GSEP_MAX_SESSIONSNoMaximum active HTTP MCP sessions500
GSEP_STORAGE_PATHNoGenome persistence path~/.gsep-mcp
GSEP_GENOME_TTL_MSNoExpire idle cached genomes after this many milliseconds3600000
GSEP_SESSION_TTL_MSNoExpire idle HTTP MCP sessions after this many milliseconds1800000
GSEP_GATEWAY_ENABLEDNoEnable OpenAI-compatible /v1 gateway in HTTP modetrue
GSEP_HTTP_AUTH_REQUIREDNoRequire API key validation for HTTP transporttrue
GSEP_KEY_VALIDATION_URLNoAPI key validation endpoint
GSEP_HTTP_AUTH_FAIL_OPENNoAllow requests if validation service is unreachablefalse
GSEP_GATEWAY_AUTH_REQUIREDNoRequire API key validation for gateway requests (follows HTTP_AUTH_REQUIRED by default)
GSEP_SESSION_CLEANUP_INTERVAL_MSNoCleanup interval for expired sessions and genomes60000

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": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
gsep_chatA

Send a message through the full GSEP pipeline. Runs C3 prompt injection scan, enhanced LLM call with evolved genes, C4 behavioral immune check, C5 action firewall, fitness tracking, and autonomous evolution. Returns the protected response.

gsep_scan_inputA

Scan user input with C3 Content Firewall (53 patterns). Detects prompt injection, role hijacking, data exfiltration attempts, encoding evasion, and more. Use this before sending any external content to your LLM.

gsep_scan_outputA

Scan LLM output with C4 Behavioral Immune System (6 checks). Detects if the response was infected by Indirect Prompt Injection — system prompt leakage, role confusion, data exfiltration patterns, and more.

gsep_scan_actionsA

Scan LLM response for dangerous or destructive actions with C5 Action Firewall (80+ patterns). Classifies actions as safe/caution/destructive/critical. Permanently blocks rm -rf, DROP DATABASE, disk wipes, etc.

gsep_before_llmA

Middleware hook to run before any agent calls an LLM. Returns enhanced_prompt, sanitized_message, and C3/security status. Use this when GSEP-MCP protects an existing external agent rather than owning the LLM call.

gsep_after_llmA

Middleware hook to run after an external LLM responds. Runs C4 behavioral immune checks, records fitness, and returns safe_response before content is shown to users or tools.

gsep_before_toolB

Middleware hook to run before an agent executes a tool, shell command, database query, filesystem action, or API call. Uses C5 Action Firewall to block destructive actions before execution.

gsep_after_toolA

Middleware hook to run after a tool returns external content and before that content is fed back into an agent/LLM. Scans for prompt injection and dangerous action instructions.

gsep_get_statusA

Get genome health, fitness scores, drift status, evolution generation, and security stats. Omit genome_id to list all active genomes.

gsep_record_feedbackA

Record user satisfaction feedback for a genome. Positive signals reinforce current gene configuration. Negative signals trigger evolution on the next cycle.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4/5.0

Scored across 10 tools

Disambiguation5/5

Each tool has a distinct role in the security pipeline: scanning input, output, actions; middleware hooks; chat; status; and feedback. No two tools serve the same purpose; even similar-sounding tools like gsep_scan_input and gsep_before_llm are clearly differentiated by their descriptions.

Naming Consistency5/5

All tools follow the 'gsep_' prefix with a verb_noun pattern (e.g., scan_input, get_status, record_feedback) or preposition_noun (before_llm, after_tool). The naming is consistent, descriptive, and predictable.

Tool Count5/5

With 10 tools, the server is well-scoped for its domain of LLM security pipeline. Each tool addresses a specific step or integration point, neither too few nor too many.

Completeness5/5

The tool set covers the complete lifecycle: input scanning, action scanning, output scanning, middleware hooks for external agents, chat pipeline, status monitoring, and feedback. There are no obvious gaps for the intended purpose.

Maintenance

ActivityInactive
ResponsivenessNo issues