get_articleGet one article (by id): full metadata, the AI abstract (description_ai), AI sentiment, and OCR text. Pass a keyword to get ~2000-char excerpts around each match instead of the full (capped) OCR.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | No | Return excerpts around matches instead of the full OCR (accent-insensitive) | |
| article_id | Yes | ||
| max_excerpts | No | Default 10, max 25 | |
| context_chars | No | Default 2000, max 5000 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the bar for adding context is lower. The description exceeds it by disclosing the return payload (metadata, AI abstract, sentiment, OCR) and, importantly, noting that OCR is 'capped' and that passing a keyword switches to ~2000-char excerpts instead. This reveals behavioral nuances beyond simple read-only status.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with no filler. The first sentence states the primary function and return fields; the second explains the keyword alternative. Every sentence earns its place, and the structure is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only single-entity fetch with no output schema, the description is complete. It tells the agent what data is returned, how the keyword parameter alters the response, and that OCR is capped. It sufficiently covers the tool's complexity without needing to explain structured return values since no output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75% (keyword, max_excerpts, context_chars have descriptions; article_id lacks one). The description compensates for article_id by saying 'by id', clarifying its role. It also adds meaning to keyword by explaining it returns excerpts, and references the ~2000-char excerpt size aligned with context_chars default. This adds value beyond the schema without redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb ('Get') and resource ('one article by id'), then enumerates the exact returned fields (full metadata, AI abstract, AI sentiment, OCR text). This makes the tool's purpose unmistakable and distinguishes it from search-related siblings like search_articles, which are about finding articles rather than retrieving a single one's full details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states the tool fetches a single article by ID, implying it is intended when you have an article ID and need complete details. It does not explicitly mention alternatives or exclusions, but given the sibling tools (e.g., search_articles, get_document), the context of retrieving one specific article by ID is evident. The phrase 'Get one article (by id)' sets the appropriate usage boundary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or analytical dimension: get_* tools are specific to item categories (article, audiovisual, document, image, publication, reference) or specific analyses (cooccurrence, field distribution, lexical metrics, semantic map, sentiment distribution, similar items, temporal distribution, topic distribution). Search tools are clearly separated by subset, with generic 'search' for cross-category discovery and search_* for filtered queries. The only potential overlap between 'fetch' and get_* is resolved by 'fetch' returning a standard format while get_* tools provide category-specific extra metadata.
All tools follow a consistent verb_noun pattern: fetch, get_*, list_*, search_*. The verbs are clear and the nouns precisely indicate the resource or action. There are no mixed conventions (e.g., no camelCase or inconsistent verb styles), making the naming predictable and easy to navigate.
At 34 tools, the count is well above the 25+ threshold that the rubric flags as too many. While the server's comprehensive scope for a digital archive with multiple subsets and analytical features explains the high count, the sheer number may overwhelm agents and makes the toolset feel heavy rather than well-scoped. Some grouping or consolidation (e.g., merging distribution tools or providing a single fetch with optional detail levels) could reduce the load without losing functionality.
The tool surface provides complete coverage for the domain: every content type (articles, publications, references, documents, audiovisual, images, index) has both search and get/retrieve tools, plus listing tools for key vocabularies. Analytical tools for statistics, distributions, sentiment, topics, and similarity are fully realized. There are no obvious dead ends—users can discover, retrieve, and analyze all parts of the collection, and even gaps in the underlying data (e.g., limited OCR coverage) are explicitly surfaced via tools like get_collection_stats.