gene_annotationsQuickGO (EBI) — list the Gene Ontology (GO) annotations for a gene/protein, identified by UniProt accession (e.g. "P04637"). Returns GO ids, names, aspect, evidence, and taxon. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max annotations (default 25, max 100). | |
| gene_product_id | Yes | A UniProt accession, e.g. "P04637". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond the annotations by stating it is from QuickGO (EBI), is keyless, and lists the types of data returned (GO ids, names, aspect, evidence, taxon). Annotations already indicate safety, but the description adds specific behavioral context.
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 that front-loads the core purpose, followed by a clarifying example. No extraneous 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?
Given the tool has only 2 parameters, no output schema, and rich annotations, the description is sufficiently complete. It explains the source, required identifier, and what fields are returned, covering the essential needs for an agent to use the tool.
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 100%, so the schema fully documents both parameters. The description adds context (e.g., 'identified by UniProt accession') but does not provide additional meaning beyond the schema, which justifies the baseline score of 3.
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 uses a specific verb ('list'), identifies the resource ('Gene Ontology annotations'), and specifies the key identifier ('UniProt accession'). It clearly distinguishes the tool's purpose from its siblings, which are mostly unrelated.
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 provides a clear usage context (need GO annotations for a UniProt ID) and includes an example, but it does not explicitly state when not to use this tool or mention alternatives among siblings.
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.
While many tools target distinct resources, there is notable overlap between ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and between discovery tools (discover_tools vs suggest_questions). The Polymarket prediction tools also have blurred boundaries (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage). This overlap can confuse an agent trying to select the right tool.
Tool names consistently use snake_case and are mostly descriptive. However, naming patterns vary: some start with verbs (list_subscriptions, validate_claim), others with nouns (ask_pipeworx, bet_research). The memory tools use single-word imperatives (remember, recall, forget), which differ from the multi-word pattern. Overall, it's readable but not rigidly consistent.
34 tools is on the high side for a single server, but the breadth of domains (data access, prediction markets, biotech, subscriptions) justifies the count. Some tools feel peripheral (gene_annotations, generate_llms_txt) and could be split off, making the set slightly bloated for the core purpose of business/financial data and prediction markets.
The tool surface covers a wide range of data sources (SEC, FDA, FRED, patents, news) and prediction market analysis comprehensively. However, there are minor gaps: no direct web search tool, no tool for fetching a specific SEC filing by accession (though ask_pipeworx may cover it). The set supports most core workflows without dead ends.