searchAutocomplete-style search across gnomAD genes and variants by free-text query; returns matching Ensembl gene IDs and symbols. Use to resolve partial gene names or symbols before calling gene or variant.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| gene_search | No | Gene search results |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "BRCA1"
+ },
+ {
+ "query": "rs1801133"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "gene_search": {
+ "description": "Gene search results",
+ "items": {
+ "properties": {
+ "ensembl_id": {
+ "description": "Ensembl gene ID",
+ "type": "string"
+ },
+ "symbol": {
+ "description": "Gene symbol",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context beyond annotations by explaining the 'autocomplete-style' matching behavior and the specific return format (Ensembl IDs and symbols). This is valuable but does not disclose additional edge cases or limitations, so a 4 is appropriate.
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 zero waste. The first sentence states the action, scope, and output; the second provides usage guidance. Every word earns its place, and key information is 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 one-parameter search tool with a rich output schema and strong annotations, the description covers purpose, usage, and return format sufficiently. It explains that this is a resolver for partial identifiers, which is complete for the tool's intended context.
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?
The schema has 0% description coverage for the 'query' parameter, so the description carries the burden. It compensates by defining the query as 'free-text' and specifying it can resolve 'partial gene names or symbols'. The schema examples show gene names and rs IDs, but the description itself adds the crucial semantics of partial matching. This is meaningful but not exhaustive, warranting a 4.
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 performs an 'Autocomplete-style search across gnomAD genes and variants by free-text query' and specifies the output as 'matching Ensembl gene IDs and symbols'. It also distinguishes itself from sibling tools by explicitly framing it as a resolver to use 'before calling gene or variant'.
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 explicit when-to-use guidance: 'Use to resolve partial gene names or symbols before calling gene or variant'. This implies when not to use it (when you already have exact identifiers) and names the alternative tools (gene, variant), giving clear contextual direction.
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.
The set mixes several overlapping query surfaces: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded/deep_research/discover_tools/suggest_questions all serve related retrieval/discovery purposes, and the five polymarket_* tools have similar opportunity-finding goals. Only the unusually detailed descriptions save some tools from misselection; an agent would struggle to quickly pick the right one.
Names are uniformly snake_case and mostly follow a verb_noun or prefixed_noun pattern (ask_pipeworx, validate_claim, polymarket_edges, scan_dependency). Minor inconsistencies exist — bare nouns like gene/variant/search sit alongside compound names like generate_llms_txt, and the pipeworx_ prefix isn't applied to ask_pipeworx/deep_research — but the overall style is recognizable and predictable.
36 tools is too many for a coherent server, especially since the domains are largely unrelated: 5 gnomAD genomics tools, 20+ Pipeworx/Polymarket data tools, memory CRUD, subscription management, and a couple of web-dev utilities. The count doesn't align with a single obvious scope and would overwhelm an agent selecting among them.
Within the major subdomains coverage is strong: memory has remember/recall/forget, subscriptions have full lifecycle tools, and Polymarket has edge detection plus fill-risk checking. However, there are notable gaps — no tool to fetch a pipeworx:// citation URI despite deep_research promising resolvable citations, and the gnomAD surface lacks batch queries, coverage, or constraint data for a server named Gnomad.