get_proteinGet a trimmed Human Protein Atlas profile for one protein by Ensembl gene id (e.g. "ENSG00000146648"): gene, description, protein class, biological process, molecular function, RNA tissue specificity/distribution, subcellular location, and disease involvement. Use search_genes to find the Ensembl id. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| 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"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, conveying that the tool is safe and idempotent. The description adds behavioral context by stating the tool returns a 'trimmed' profile (implying a subset of data), listing the specific fields, and noting 'Keyless' (likely meaning no authentication required). This adds value beyond the 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 extremely concise: two sentences that front-load the purpose with the verb 'Get'. It wastes no words and includes a code example and a usage hint. Every sentence earns its place.
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's simplicity (one parameter, no output schema), the description is complete. It enumerates the return fields and mentions keyless access. There are no obvious gaps for a user to understand input, output, and behavior.
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% for the single parameter 'ensembl_id', so the schema already documents it fully. The description adds an example ID ('ENSG00000146648') which helps illustrate the format, but does not provide additional semantic meaning. According to guidelines, baseline is 3 when coverage is high, and the example is a minor improvement.
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 action ('Get') and the resource ('trimmed Human Protein Atlas profile for one protein by Ensembl gene id'). It lists the specific data fields returned and provides an example Ensembl ID, making the tool's purpose unmistakable. It also distinguishes itself from the sibling tool 'search_genes' by directing users to use that tool to find the Ensembl ID.
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 explicitly mentions using 'search_genes' to find the Ensembl ID, providing a clear alternative for discovery. However, it does not specify when not to use this tool or other potential alternatives beyond that. For a simple lookup tool, this level of guidance is sufficient but not exhaustive.
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.