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Glama

self-hosted-ai

by org.sovgrid

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
NameRequiredDescriptionDefault
slugYesArticle slug as returned by search_blog (e.g. 'setup-llm-inference-setup'). Lower-case, hyphenated.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublic URL of the article
bodyNoFull article body in Markdown
dateNoPublication date (ISO 8601)
slugYesArticle slug
tagsNoTopic tags assigned to the article
errorNoSet to 'article_not_found' if no article matches the slug
titleNoArticle title
word_countNoWord count of the article body
descriptionNoShort article description
quality_classNoEditorial content class (e.g. 'Ephemeral', 'Evergreen'). Empty if not classified.
quality_scoreNoBuild-time quality score from the editorial pipeline (unbounded weighted composite across 13 signals, higher is better; thresholds depend on style)
quality_styleNoEditorial 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.

  1. Changed1 schema field changed
    • 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."
  2. First observed

TDQS

A4.1/5.0
Behavior5/5

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

The description discloses key behaviors beyond annotations: it returns the article body in Markdown plus metadata, and specifies the exact error behavior when a slug is not found (returns an Article with error='article_not_found' and defaults). This is valuable context that annotations (readOnlyHint, idempotentHint) do not provide.

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?

Two sentences, zero padding. The first sentence states the primary action, and the second covers edge-case behavior. Information is front-loaded and every sentence earns its place.

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 single-parameter read tool with an output schema, the description covers purpose, return content, and not-found behavior. It is fully sufficient for an agent to invoke correctly without requiring additional details about response structures or side effects.

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 100% coverage, including an example and format note for the slug. The description adds no additional parameter semantics beyond restating 'by its slug', so it cannot exceed the baseline of 3 for schema-heavy tools.

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 'Retrieve the full content of a blog article by its slug', using a specific verb and resource. It distinguishes itself from siblings like search_blog and list_tags by focusing on full-content retrieval for a single known article.

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

Usage Guidelines2/5

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

The description provides no explicit guidance on when to use this tool vs alternatives. It does not mention that search_blog should be used first to obtain a slug, or that this tool is not for searching/listings. The only contextual hint is in the input schema, not the description itself.

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 function: SGLang config diagnosis versus blog article browsing (search, list tags, get article). No overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: diagnose_sglang, get_article, list_tags, search_blog.

Tool Count4/5

With 4 tools, the count is within the well-scoped range (3-15). However, the server mixes two separate domains, making the scope slightly thin.

Completeness3/5

The blog tools cover reading needs (search, list tags, get article), but the SGLang diagnostic tool is a single operation with no update or management tools. For a 'self-hosted-ai' server, broader AI operations are missing.

Resources