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biocontext-ai

nucleotide_archive_mcp

search_rna_studies

Search for RNA sequencing studies by disease, tissue, and organism to find relevant datasets across multiple species.

Instructions

Search for RNA sequencing studies by disease, tissue, and organism.

When to use this tool: Primary search tool for finding RNA-seq datasets when you know the disease/condition OR tissue/cell-type you're interested in. Use search_studies_by_keywords() for broader searches by biological processes or methodology.

Default behavior: Searches across ALL organisms unless you specify one. This helps find relevant datasets across multiple species (human, mouse, rat, etc.).

Search tips:

  • Try different keyword variations if no results (e.g., "ALS" vs "amyotrophic lateral sclerosis")

  • Try broader terms (e.g., "neurodegeneration" instead of specific disease)

  • Search all organisms first (organism=None), then filter to specific species if needed

  • Use tissue parameter to narrow results to specific anatomical sites

Returns

dict Search results containing: - count: How many total studies match your search - returned: How many studies are in this response - studies: List of matching studies with titles, sample counts, and publication info - query_used: The exact search query that was executed - filters: What filters were applied to your search

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diseaseNoDisease/condition keywords to search for (e.g., 'cancer', 'ALS'). Matched against disease and study_title fields
organismNoOrganism to search for. Use common names like 'human' or 'mouse', or scientific names like 'Homo sapiens'. Leave as None to search across all species
technologyNoRNA sequencing technology type: 'bulk' for standard RNA-Seq, 'single-cell' for scRNA-seq, 'small-rna' for miRNA/small RNA, 'ribo-seq' for ribosome profiling, 'rna-all' for any RNA technology. Set to None to search ALL RNA technologies without filtering
tissueNoTissue or cell type keywords to search for (e.g., 'brain', 'liver'). Matched against tissue_type and study_title fields
library_strategiesNoAdvanced: Specific sequencing strategies to filter by (e.g., ['RNA-Seq']). Overrides the technology preset. Call list_library_types() first to see all available options
library_sourcesNoAdvanced: Specific library source materials to filter by (e.g., ['TRANSCRIPTOMIC']). Overrides the technology preset. Call list_library_types() first to see all available options
limitNoHow many studies to return. Use 20 for initial searches, increase if needed. Set to 0 to get all results (up to system limits)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description fully explains default behavior (searches across all organisms unless specified) and gives search tips. It does not mention destructive actions (irrelevant for search) but adequately covers behavioral traits. The return structure is described. A score of 4 reflects good transparency without explicit safety or rate-limit details, which are not essential here.

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

Conciseness4/5

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

The description is well-structured with clear sections (purpose, when to use, default behavior, search tips, return format). It is somewhat verbose but each part adds value. Front-loading is effective. Slight reduction in redundancy would improve conciseness.

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 the complexity (7 parameters, 0 required, high schema coverage, and an output schema), the description is complete. It covers all essential aspects: purpose, usage context, behavioral details, parameter guidance, and return values. An agent can correctly invoke this tool based on the description alone.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining parameter roles (e.g., disease/tissue matched against study_title), clarifying the technology parameter vs library strategies, and providing search tips. This enhances understanding beyond the schema descriptions.

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 clearly states the tool's purpose: 'Search for RNA sequencing studies by disease, tissue, and organism.' It specifies the main parameters and distinguishes itself from the sibling tool search_studies_by_keywords, which is used for broader searches.

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

Usage Guidelines5/5

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

The description explicitly provides a 'When to use this tool' section, stating it is the primary search tool when you know disease/condition or tissue/cell-type, and directs to use search_studies_by_keywords() for broader searches by biological processes or methodology. This clearly differentiates usage from alternative tools.

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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