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Get a first-party essay

get_article
Read-onlyIdempotent

Get one complete essay by slug, including its clean Markdown-like body, metadata, licence context and canonical URL. Use this after list_articles or search_articles has returned the slug you need.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesArticle slug, e.g. 'the-stopwatch-and-the-exam'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesFull Markdown-like article body.
slugYes
titleYes
topicsYes
api_urlYes
summaryYes
languageYes
modifiedYes
publishedYes
resource_uriYes
canonical_urlYesCite this URL.
translation_keyNo

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and no destructive behavior, so the safety profile is covered. The description adds useful context about the response contents (Markdown-like body, metadata, licence context, canonical URL), but does not disclose additional behavioral traits such as error cases or availability constraints.

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 with no filler: the first states what the tool returns and the second gives the operational sequence. The key identifier concept ('by slug') 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-only tool with a rich annotation set and an output schema, the description covers the essential invocation context. It tells the agent what to expect in the response and how to obtain the required slug, leaving no critical gaps.

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

Parameters4/5

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

The schema already fully documents the slug parameter with an example, so the baseline is 3. The description adds value by explicitly connecting the slug to the output of list_articles or search_articles, telling the agent where the value should come from rather than treating it as arbitrary input.

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 uses a specific verb ('Get') and resource ('one complete essay'), and clearly distinguishes this from list/search operations by emphasizing a single essay fetched by slug. The phrase 'by slug' and 'one complete' make the tool's role unambiguous relative to siblings such as list_articles and search_articles.

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 explicitly states when to use the tool: after list_articles or search_articles has returned the needed slug. It provides clear sequencing context, though it does not explicitly name alternatives or state when not to use this tool beyond the implied single-record scope.

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

A4.1/5.0
Disambiguation4/5

Most tools are cleanly separated by content type and the list_/get_ pairs are predictable. The main ambiguity is among search, search_all, and search_articles: search claims to cover the 'whole corpus' while search_all actually expands to essays, labs, claims, and the Homeric Atlas, so an agent could select the narrower search and miss content.

Naming Consistency5/5

Every tool follows the same snake_case verb_noun pattern: calculate_*, get_*, list_*, and search_*. Even the three search variants are predictable from their suffixes, so there are no mixed naming conventions.

Tool Count2/5

30 tools is above the preferred MCP size and creates real selection burden for agents, even though the multi-surface knowledge scope explains the volume. The set is systematic rather than bloated, but 25+ tools is still too many for a typical server surface.

Completeness4/5

Each content surface has browse, fetch, and search coverage, and get_related plus get_overview provide cross-cutting navigation. The only meaningful gap is that the relationship between search and search_all is not fully disjoint, which can create a dead-end if the wrong search tool is chosen first.