latest_articlesList the most recently published eLife articles (newest first). eLife is the open-access journal for life sciences & biomedicine. Returns id, title, type, DOI, publication date, and authors. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 15, max 100). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. Description adds value by confirming keyless access, ordering (newest first), and exact return fields, providing context beyond annotations.
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?
Three focused sentences: action and ordering, journal context, return fields and keyless. No redundancy, well front-loaded.
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 read-only tool with one parameter and no output schema, the description covers purpose, ordering, return values, auth status. Annotations handle safety/hints.
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?
Only one parameter (limit) with schema describing default and max. Description does not add additional semantics. Given 100% schema coverage, baseline 3 is appropriate.
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 lists most recently published eLife articles newest first, specifies return fields (id, title, type, DOI, publication date, authors), and notes keyless access. It distinguishes from siblings like get_article or search_articles.
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?
Implied use is for simple listing of recent articles. However, no explicit guidance on when not to use (e.g., when search is better) or comparison with siblings like search_articles or get_article.
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 has a clearly distinct purpose. Tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are differentiated by reliability mode; entity_profile vs compare_entities serve single vs multi-entity; and memorization, subscription, and search tools occupy separate operational niches. No two tools could be easily confused.
Tool names follow no consistent pattern. Some use verb_noun (ask_pipeworx, resolve_entity, validate_claim), others are noun phrases (polymarket_arbitrage, ai_visibility_check), and some mix verb+noun with underscores inconsistently (generate_llms_txt, scan_competitor_ai_presence). This lack of uniformity makes the surface harder to navigate.
34 tools is borderline high. The server spans multiple domains (data query, prediction markets, eLife, memory, subscriptions), and each domain gets several tools, making the overall surface feel bloated. While individual tools are justified, the total count strains discoverability and hints at scope creep.
The tool set is incomplete relative to its stated breadth. For a server named 'Elife', only 3 tools actually serve eLife; the rest are dominated by Pipeworx and Polymarket. Within the query/data domain, coverage is deep but lacks write/modify tools. Prediction market analysis lacks execution tools (no order placement). This leaves clear gaps for agents that need to act on the data.