pages_by_nameFind EOL page id(s) for an exact scientific name.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | e.g. "Panthera leo" |
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The scientific name searched for | |
| matches | Yes | EOL page results matching the exact name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive, covering behavioral safety. The description adds no further behavioral details (e.g., rate limits, authentication, or what happens on missing names). It does not contradict 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?
The description is a single, concise sentence (10 words) that communicates the core purpose efficiently. No extraneous information, and the key verb and resource are 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?
Given the tool's simplicity (1 required parameter, output schema present), the description adequately explains input and output. It could mention whether multiple IDs are possible or what happens if the name is not found, but this is minor given the output schema likely covers return format.
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 coverage is 100% and the parameter description in the schema ('e.g. "Panthera leo"') is already clear. The tool description ('exact scientific name') adds no new semantic value beyond what the schema provides, meeting baseline expectations.
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 a specific action ('Find'), a specific resource ('EOL page id(s)'), and a specific input condition ('exact scientific name'). This distinguishes it from sibling tools like get_page (which retrieves a page by ID) and search (which supports partial matches).
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 implies usage for exact scientific name lookups but does not explicitly state when to use this tool over alternatives (e.g., search, search_within) or when not to use it (e.g., for common names). No exclusions or prerequisites are mentioned.
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, with detailed descriptions that eliminate ambiguity. Even similar tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by use case (casual, high-stakes, multi-faceted). The Polymarket and EOL tool suites are internally distinct.
Naming mostly follows snake_case with verb_noun or prefix patterns, but there is inconsistency: e.g., 'ask_pipeworx' vs 'bet_research' vs 'deep_research'. The Polymarket and memory tool groups are internally consistent, but overall the server mixes conventions across sub-domains.
34 tools is on the high side, but the server covers multiple domains (EOL taxonomy, Pipeworx data, Polymarket betting, memory, subscriptions). The count is borderline excessive for a focused server; meta-tools like discover_tools and suggest_questions help, but the sheer number can overwhelm an agent.
The tool set covers many data sources and analysis tasks well, but the server name 'Eol' implies a biological taxonomy focus, which is underserved (only 4 tools). For the broader implicit purpose of a research assistant, there are notable gaps like open-web search, image analysis, or document management.