search_termsQuickGO (EBI) — search Gene Ontology (GO) terms by keyword. Returns matching GO terms (id + name) for a free-text query like "apoptosis". Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 15, max 100). | |
| query | Yes | Free-text keyword to search GO terms, e.g. "apoptosis". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds the 'Keyless' detail (no API key needed) and specifies the return format (id + name) and source (QuickGO EBI). This provides useful behavioral context beyond annotations, though rate limits or pagination are not mentioned.
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 no wasted words. It front-loads the main action and adds key details efficiently.
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 search tool with two parameters, full schema coverage, and rich annotations, the description covers the purpose, return format, source, and authentication requirement. It is nearly complete for the complexity level, though it could mention the maximum limit (already in schema) or potential empty results.
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%, but the description adds value by characterizing 'query' as a free-text keyword search with an example ('apoptosis') and noting the return format. This helps the agent understand parameter semantics beyond the schema's description.
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 searches Gene Ontology terms by keyword and returns matching terms (id + name). It uses a specific verb and resource. However, it does not explicitly differentiate from sibling tools like 'get_term' or 'search_within', missing the chance to help an agent choose between them.
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 no guidance on when to use this tool versus alternatives. It mentions 'Keyless' (indicating no authentication), but does not specify prerequisites, exclusions, or contexts where other tools (e.g., 'get_term') would be more appropriate.
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.