Sovereign AI Blog

get_article

Read-onlyIdempotent

Retrieve the full content of a blog article by its slug.

Returns the article body (Markdown) plus metadata. If the slug does not
match any article, returns an Article with `error='article_not_found'`
and other fields at their defaults.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes	Article slug as returned by search_blog (e.g. 'setup-llm-inference-setup'). Lower-case, hyphenated.

Output Schema

TableJSON Schema

Name	Required	Description
`url`	No	Public URL of the article
`body`	No	Full article body in Markdown
`date`	No	Publication date (ISO 8601)
`slug`	Yes	Article slug
`tags`	No	Topic tags assigned to the article
`error`	No	Set to 'article_not_found' if no article matches the slug
`title`	No	Article title
`word_count`	No	Word count of the article body
`description`	No	Short article description
`quality_class`	No	Editorial content class (e.g. 'Ephemeral', 'Evergreen'). Empty if not classified.
`quality_score`	No	Build-time quality score from the editorial pipeline (unbounded weighted composite across 13 signals, higher is better; thresholds depend on style)
`quality_style`	No	Editorial style category (e.g. 'best_practice_learnings', 'werthaltige_code_beispiele'). Empty if not categorised.

Schema Changelog

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

Changed1 schema field changedJul 20, 2026

changedInput schema / properties / slug / description

Previous value: -"Article slug as returned by search_blog (e.g. 'setup-mistral-sglang-setup'). Lower-case, hyphenated."New value: +"Article slug as returned by search_blog (e.g. 'setup-llm-inference-setup'). Lower-case, hyphenated."

First observedApr 30, 2026

TDQS

A4.3/5.0

Behavior5/5

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

The description adds valuable behavioral context beyond the annotations: it specifies the return format (Markdown body plus metadata) and the exact error behavior when the slug is not found. This is particularly helpful for an agent to handle failure cases gracefully.

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 two sentences, front-loaded with the action, and every sentence adds value. It avoids redundancy and is easy to parse.

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?

For a simple retrieval tool with one parameter, the description covers the core function, return value, and error case. The output schema provides additional structural details, so the description is complete and well-suited for the tool's complexity.

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 already provides a rich description of the slug parameter, including an example and format guidance (lower-case, hyphenated). The description itself adds no new semantics about the parameter, so the baseline of 3 applies since schema coverage is 100%.

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 retrieves the full content of a blog article by slug, which distinguishes it from sibling tools like search_blog that likely list or search articles. The verb 'Retrieve' and resource 'blog article' are specific, leaving no ambiguity.

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

Usage Guidelines3/5

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

The description implies usage when you have a slug ('by its slug'), but it does not explicitly mention alternatives or when to use this tool versus search_blog or list_tags. The parameter schema references search_blog, but that is outside the description itself, so guidance is implied rather than explicit.

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.

Resources

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