get_pageFetch a taxon page by EOL id (synonyms, common names, hierarchy summary).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | EOL page id (from /search) | |
| detail | No | Include data objects (texts, images). Default false. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, non-destructive, and open-world. The description adds value by specifying the returned content (synonyms, common names, hierarchy summary), enhancing transparency 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?
The description is a single, concise sentence with only 12 words. It efficiently conveys the tool's purpose without unnecessary information.
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 two parameters and an output schema, the description is complete. It covers what the tool does and what it returns. No additional context seems necessary.
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%, so the description does not add new parameter information. It merely echoes the schema by mentioning 'EOL id', but the schema already provides detailed descriptions for both parameters.
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 fetches a taxon page by EOL id, with specific content (synonyms, common names, hierarchy summary). This precise verb-resource-object combination distinguishes it from sibling tools like pages_by_name or search.
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 use when an EOL id is available but does not explicitly state when to use this tool versus alternatives like pages_by_name or search. No exclusion criteria or prerequisites are provided.
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.