dnnsec
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 |
|---|---|
| verify_model_integrityB | Verifies neural network model weights and adversarial robustness checksums. |
| check_adversarial_robustnessB | Performs adversarial attack vulnerability screening for neural architecture. |
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 2 tools
The two tools have overlapping purposes: both verify model integrity and check adversarial robustness, making it unclear when to use one versus the other. An agent would struggle to decide which tool handles a given security assessment request.
Both tool names follow a consistent verb_noun pattern (verify_model_integrity, check_adversarial_robustness), though the verbs 'verify' and 'check' are near-synonyms, which slightly reduces clarity.
With only two tools, the server feels thin for a domain as broad as neural network security. The count is at the extreme low end, offering minimal coverage for the stated purpose.
The tool surface is severely limited: it covers verification and robustness screening but lacks common operations like model hardening, attack generation, report generation, or remediation. The domain is not fully covered.