Get index entry details
get_index_entryGet full details of an index entry by id (raw dataset columns, French names — Titre, Prénom, Coordonnées…).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| entry_id | Yes |
get_index_entryGet full details of an index entry by id (raw dataset columns, French names — Titre, Prénom, Coordonnées…).
| Name | Required | Description | Default |
|---|---|---|---|
| entry_id | Yes |
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 safety profile is known. The description adds useful context about the returned data (raw dataset columns, French names), but it does not disclose error behavior, response shape, or potential edge cases. This is acceptable but not exceptional given the annotation coverage.
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 a single, well-structured sentence. It leads with the core action and resource, then provides a parenthetical with concrete field examples. Every word adds value, and there is no redundant or filler content.
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 simple, single-parameter, read-only tool, the description is largely sufficient: it names the input, the output nature, and clarifies the target resource. It does not mention possible errors or how to find the correct entry_id, but given the sibling set and the tool's low complexity, this is a minor gap.
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 description only says 'by id', which is essentially a restatement of the parameter name entry_id. It does not explain how the ID is obtained, whether it is the internal database ID or a document ID, or what constraints apply beyond the schema's integer type. With 0% schema description coverage, the description should compensate but adds minimal semantic value.
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's action ('Get'), the resource ('full details of an index entry'), and the lookup method ('by id'). It also gives concrete examples of returned fields (Titre, Prénom, Coordonnées), which distinguishes it from sibling get_* tools that target articles, documents, or references.
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 phrase 'by id' indicates the tool is used when the agent has a specific index entry ID and needs detailed information. It provides clear context for when to invoke the tool, though it does not explicitly mention alternatives or when not to use it.
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.