top_tissuesList a protein's top-expressing human tissues by RNA expression (nTPM), highest first, for one Ensembl gene id. Use search_genes to find the Ensembl id. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max tissues to return (default 10). | |
| ensembl_id | Yes | An Ensembl gene id like "ENSG00000146648". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "ensembl_id": "ENSG00000146648"
+ },
+ {
+ "ensembl_id": "ENSG00000146648",
+ "limit": 5
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. The description adds that results are ordered 'highest first', data source is 'RNA expression (nTPM)', and notes 'Keyless' for no authentication. No contradictions with 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?
Two sentences, front-loaded with the main action. Every sentence adds value: first describes the tool, second gives usage guidance and notes keyless. No redundancy.
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?
No output schema exists, and the description does not specify the format of the returned list (e.g., fields like tissue name and nTPM value). It implies human tissues only. Adequate but incomplete for a read 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 has 100% coverage for both parameters, including default for limit. Description does not add significant meaning beyond the schema; it mentions ordering but that is not parameter-specific. Baseline 3 is appropriate.
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 verb 'List' and resource 'a protein's top-expressing human tissues by RNA expression (nTPM)' for one Ensembl gene id. It mentions search_genes as a prerequisite but does not explicitly differentiate from sibling tools.
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?
Explicitly advises to use search_genes to find Ensembl id before using this tool. Provides clear context for when to use, but no explicit when-not-to-use statements.
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.
Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all handle routed research queries, while ai_visibility_check and scan_competitor_ai_presence overlap directly and the six Polymarket tools form a dense, easily confused cluster. The descriptions are detailed, but an agent will frequently struggle to pick the right tool among near-duplicate research and prediction-market options.
Names are readable and mostly snake_case, with useful prefixes like ask_pipeworx_ and polymarket_. However, conventions are mixed: some are verb_noun (search_genes, get_protein, generate_llms_txt), some are bare verbs (remember, recall, forget), and some are noun phrases (entity_profile, recent_changes, top_tissues). There is no single predictable pattern.
34 tools is above the 25+ threshold for a heavy, hard-to-navigate set, and most of them are not related to the server's stated 'Protein Atlas' identity. Only three tools actually concern proteins, while the rest form a general data-research, Polymarket, memory, and subscription toolkit that feels like several servers merged into one.
For a Protein Atlas server, the surface is severely incomplete: only search_genes, get_protein, and top_tissues cover HPA, leaving pathology, cell-line, single-cell, blood, and other major HPA dimensions unaddressed. If the intended domain is instead the broader Pipeworx data router, the protein tools are an odd vestige and the completeness story is still muddled by overlapping meta-tools.