Read the best abstract available for a paper. Use with a DOI or with author_name + candidate_index + paper_index after author_papers. If you pass engines, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | ||
| engines | No | ||
| author_name | No | ||
| paper_index | No | ||
| candidate_index | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states it reads the best abstract and lists allowed engine values. It does not disclose fallback behavior if no abstract is found, whether it queries all engines simultaneously, or explicitly confirm it is a read-only operation beyond the word "Read."
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 with clear front-loading of purpose. The second sentence covers parameter usage without redundancy. Every sentence earns its place, and the engine list is necessary for correct invocation.
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 the tool has 5 optional parameters, no annotations, and an output schema, the description covers the essential invocation patterns and engine constraints. It doesn't explain precedence when both DOI and author parameters are provided, or error behavior, but the output schema likely covers return values. The context is adequate for the tool's complexity.
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 0%, so the description must compensate. It explains the two usage modes (DOI vs author_name + candidate_index + paper_index), and details the `engines` parameter with a comma-separated list of accepted values. It leaves `candidate_index` and `paper_index` somewhat implicit, but referencing "after author_papers" provides useful context.
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 function with a specific verb+resource combination: "Read the best abstract available for a paper." It distinguishes itself from siblings like scholarfetch_article_text (full text) and scholarfetch_search (search) by focusing specifically on abstract retrieval.
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?
Provides explicit usage paths: "Use with a DOI or with author_name + candidate_index + paper_index after author_papers." This gives clear context for when to use which parameters. However, it does not explicitly state when NOT to use this tool or alternatives, though the pipeline reference implies after author_papers.
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 operation: searching, DOI lookup, author disambiguation, paper listing, abstract/full-text retrieval, reference expansion, and saved-list management. No two tools overlap in purpose, and the descriptions clearly differentiate entry points (DOI vs. author vs. keyword) and output types.
All tools share the 'scholarfetch_' prefix and use lowercase snake_case, but the second part mixes nouns (abstract, article_text, references) with verb phrases (saved_add, doi_lookup, search). This is a minor deviation from a strict verb_noun pattern, but the overall pattern remains predictable and readable.
With 12 tools, the server is well-scoped for academic literature retrieval and management. Each tool has a clear role, and the count is within the ideal range, providing a complete workflow without unnecessary bloat.
The tool surface covers the full research process: discovery (search, DOI lookup), author exploration (candidates, papers), reading (abstract, full text), citation traversal (references), and library management (saved add/list/remove/export). No obvious gaps exist for the stated purpose.