security-genome-reactor
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
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
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| genome_infer_phenotypesA | Infer latent security phenotypes from observed findings and compliance behavior, ignoring cloud labels as ground truth. |
| genome_seedC | Seed multiple candidate security genomes for one phenotype. |
| genome_evolveB | Run one deterministic evolutionary generation using rehearsal by default or caller-supplied measured fitness keyed by genome id. |
| genome_trial_planC | Translate a genome into a bounded target/policy/profile trial plan. |
| genome_lineageB | Return ancestry in parent-before-child order for a genome from a supplied population/history. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool targets a clearly distinct step in the genome workflow: inferring phenotypes, seeding candidates, evolving generations, planning trials, and tracing lineage. There is no meaningful functional overlap between any two tools.
All names share the genome_ prefix and use snake_case, which is readable and recognizable. However, the pattern is mixed: genome_infer_phenotypes is verb-object, genome_seed and genome_evolve are bare verbs, while genome_trial_plan and genome_lineage are noun phrases.
Five tools is well-scoped for this specialized domain. Each tool represents a meaningful stage in the reactor lifecycle without redundant or filler operations.
The set covers the core genome lifecycle: infer phenotypes, seed candidates, evolve generations, produce trial plans, and inspect lineage. Fitness is intentionally caller-supplied, so no evaluation tool is a necessary gap.