get_documentGet one archival document (by id): full metadata, AI description, and OCR text. Pass a keyword to get ~2000-char excerpts around each match instead of the full (capped) OCR — useful for long documents.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | No | Return excerpts around matches instead of the full OCR (accent-insensitive) | |
| document_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?
The description discloses that OCR text is capped and that keyword mode returns ~2000-character excerpts instead, which adds behavioral context beyond the readOnly/inidempotent annotations. It does not mention other potential behaviors like rate limits or errors, but the annotations already cover the safety profile, so this additional context is valuable.
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 two sentences long, front-loaded with the core purpose and then immediately explaining the optional keyword behavior. Every word contributes, with no filler or repetition, making it highly concise and well-structured.
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 get-by-id tool with no output schema, the description clearly indicates what is returned (metadata, AI description, OCR text) and explains the keyword alternative. It also notes the OCR cap, which is an important detail. The annotations and input schema fill in safety and parameter details, making the overall tool description complete for an agent to invoke correctly.
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?
The schema already documents 3 of 4 parameters with descriptions (keyword, max_excerpts, context_chars). The description adds meaning for keyword by explaining the excerpt behavior and aligns with the default context_chars. However, it does not explicitly describe max_excerpts or context_chars beyond what the schema provides, and document_id lacks any description in both. With 75% schema coverage, the description moderately compensates but does not fully enhance all parameters.
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 clearly states the tool retrieves one archival document by ID, specifying the contents (metadata, AI description, OCR text). The phrase 'archival document (by id)' distinguishes it from sibling tools like get_article or get_image, making the purpose unambiguous.
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 provides clear context: use this when you need a specific document's full details by ID. It also gives guidance for the keyword parameter, explaining when to use it (to get excerpts for long documents). However, it does not explicitly mention alternatives or when not to use this tool, so it lacks a direct exclusion statement.
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.