get_publication_fulltextFull OCR text of a publication, optionally returning ~2000-char excerpts around keyword matches (accent-insensitive; capped — see match_count vs excerpts_returned).
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | No | ||
| max_excerpts | No | Default 10, max 25 | |
| context_chars | No | Default 2000, max 5000 | |
| publication_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent annotations, the description discloses accent-insensitive keyword matching, capped excerpts, and a subtle distinction between match_count and excerpts_returned. This is valuable behavioral context that helps set expectations about output limits.
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, front-loaded sentence with no filler. It efficiently conveys the main purpose and key optional behavior in under 25 words.
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?
Given there is no output schema, the description leaves some ambiguity about the return structure (e.g., what exactly match_count and excerpts_returned are, and whether full text is always returned). It provides a hint but not a complete specification of the response.
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?
With 50% schema coverage, the description partially compensates by explaining the role of keyword (excerpts around matches) and context_chars (~2000 chars). It doesn't clarify publication_id, but that is self-evident from the tool name. The capping mention also hints at max_excerpts semantics.
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 it returns full OCR text of a publication and optionally excerpts around keyword matches. It is specific about the resource (publication) and operation, but it does not explicitly differentiate from sibling tools like get_document or get_article.
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?
Usage is implied: use when you need full OCR text or keyword-matching excerpts. However, there is no explicit guidance on when to choose this over alternatives, nor any exclusions or prerequisites.
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.