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find_differentially_expressed_genes

Retrieve the top upregulated and downregulated genes for a specific assay from the GeneLab Knowledge Graph. Identify key differentially expressed genes in spaceflight experiments.

Instructions

Return the top-N up- and down-regulated genes for a given assay_id.

This tool runs two queries on the GeneLab KG:

  1. Top-N upregulated genes (log2fc > 0, highest first)

  2. Top-N downregulated genes (log2fc < 0, lowest first)

FORMATTING INSTRUCTION: RENDER THE RESPONSE IN MARKDOWN FORMAT!

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assay_idYesAssay identifier (e.g., 'OSD-253-6c5f9f37b9cb2ebeb2743875af4bdc86')
top_nNoHow many genes to return for each of up- and down-regulated lists

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that two separate queries are run, the log2fc filtering criteria, and ordering. It also includes a formatting instruction (render in markdown). While it does not mention side effects or rate limits, for a read-only query tool this level of detail is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences and one formatting instruction. Every sentence adds value—the first states the purpose, the second details the queries, and the instruction ensures proper output. No redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that the tool has an output schema (context signal: true), the description is not required to detail return field structure. It adequately explains what is returned (top-N up/down genes) and how they are determined. With an output schema available, this completeness level is appropriate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already describes both parameters (assay_id with example, top_n with default). The tool description adds no additional parameter context beyond what the schema provides, so it meets the baseline without exceeding it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it returns top-N up- and down-regulated genes for a given assay_id, using two specific queries. This is distinct from sibling tools like create_volcano_plot or find_common_differentially_expressed_genes, so the purpose is clear and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does but offers no guidance on when to use it versus alternatives like find_common_differentially_expressed_genes. Usage context is implied by the name and description, but no explicit when-to-use or when-not-to-use information is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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