Sovereign AI Blog

diagnose_sglang

Read-onlyIdempotent

Validate an SGLang configuration for NVIDIA DGX Spark (GB10/SM121A).

Pure pattern-matching against known failure modes documented in the Sovereign AI Blog. No inference, no external calls. Returns critical issues, non-fatal warnings, and a recommended baseline config.

All parameters are optional; supply only what you have. With no inputs you get the recommended config and a 'unknown' verdict.

Input Schema

TableJSON Schema

Name	Required	Description
`hardware`	No	Hardware description (e.g. 'GB10', 'DGX Spark', 'SM121A'). Empty = skip GB10-specific rules.
`image_tag`	No	Docker image tag in use (e.g. 'lmsysorg/sglang:latest', 'lmsysorg/sglang:v0.4.0'). Empty = skip.
`mem_fraction`	No	SGLang --mem-fraction-static value (e.g. 0.88). 0.0 = skip this check.
`error_message`	No	Paste error log output here for pattern matching against known failure modes.
`attention_backend`	No	SGLang --attention-backend value (e.g. 'flashinfer', 'triton'). Empty string = skip this check.
`cuda_graph_max_bs`	No	SGLang --cuda-graph-max-bs value. 0 = skip this check.

Output Schema

TableJSON Schema

Name	Required	Description
`issues`	Yes	Critical issues that will prevent SGLang from running correctly
`verdict`	Yes	Overall verdict. 'unknown' = no inputs provided.
`warnings`	Yes	Non-fatal warnings (suboptimal but non-blocking)
`recommended_config`	Yes	Verified-good baseline config for GB10/SM121A

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedApr 30, 2026

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only and idempotent hints. The description adds valuable behavioral context: it explicitly states the tool does pure pattern-matching, makes no external calls, and returns issues, warnings, and a recommended config, including the no-input edge case.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and well-structured: it states the core purpose, explains the method, describes outputs, and covers the all-optional edge case. Every sentence contributes meaningful information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a full output schema and detailed parameter descriptions, the description appropriately covers the tool's scope, behavior, and edge cases. It is sufficient for an agent to understand when and how to invoke the tool, including the no-input scenario.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers all parameters with detailed descriptions and skip semantics. The description adds only the general note that all parameters are optional, which is helpful but not a substantial enhancement over the schema's existing parameter-level guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool validates an SGLang configuration for NVIDIA DGX Spark, specifying the exact verb and resource. It distinguishes itself from sibling content-retrieval tools by outlining its diagnostic pattern-matching purpose and return types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use the tool (validate SGLang config) and notes that all parameters are optional, implying you supply what you have. It does not explicitly list exclusions or alternative tools, but the distinct purpose makes it unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct operation: configuration validation, article retrieval, tag listing, and article search. There is no functional overlap, so an agent can easily distinguish them.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (e.g., diagnose_sglang, get_article, list_tags, search_blog), making them predictable and easy to remember.

Tool Count4/5

Four tools is a reasonable number for a focused blog server, though the inclusion of a diagnostic tool (diagnose_sglang) slightly broadens the scope. The count still feels well-scoped and not excessive.

Completeness4/5

The blog-related tools cover reading operations (get, search, browse tags) adequately; a listing endpoint is handled via search with an empty query. The diagnostic tool is an additional feature, but there are no obvious gaps in core blog functionality.

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