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biocontext-ai

nucleotide_archive_mcp

by biocontext-ai
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

search_studies_by_keywords

Find studies from the European Nucleotide Archive by searching keywords across titles and descriptions. Use for broad exploratory searches of RNA sequencing datasets.

Instructions

Search for studies using flexible keyword matching across titles and descriptions.

When to use this tool: Use this for broad exploratory searches when search_rna_studies() is too restrictive. Good for:

  • Searching by biological processes, pathways, or molecular mechanisms

  • Finding studies about specific genes, proteins, or complexes

  • Searching by methodology when you don't know the specific disease

  • General exploratory searches across many study types

Important notes:

  • Searches both study-level titles AND sample-level descriptions

  • Multi-word keywords are split: "breast cancer" searches for "breast" AND "cancer"

  • May return studies where only one sample mentions your keywords

  • Results can be broader than search_rna_studies() disease/tissue filters

Search tips:

  • Try keyword variations and abbreviations if no results (e.g., "immune response" vs "immunity")

  • Try broader or narrower terms (e.g., "transcription" vs "transcription factor binding")

  • Consider searching multiple organisms if limited results in one species

  • Use organism filter to narrow down results to specific species

Returns

dict Search results containing: - count: How many total studies match - returned: How many studies in this response - keywords_used: What keywords were searched - organism_filter: What organism filter was applied (if any) - studies: List of matching studies with accession, title, organism, and other metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYesKeywords to search for in study titles and descriptions (e.g., 'immune response', 'transcription factor'). Multi-word phrases are automatically split into individual words that must all match
include_titleNoWhether to search in study titles (recommended: keep True)
include_descriptionNoWhether to search in study descriptions. This searches sample-level descriptions and may broaden results
organismNoOptionally filter by organism. Use common name like 'human' or scientific name like 'Homo sapiens'. Leave as None to search across all organisms
limitNoHow many studies to return. Start with 20, increase if needed

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

Without annotations, the description fully discloses key behaviors: it searches both study titles and sample descriptions, splits multi-word keywords into AND conditions, may return studies with only one sample matching, and notes broader results compared to disease/tissue filters. This gives agents complete transparency.

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?

Well-structured with clear sections ('When to use', 'Important notes', 'Search tips') and a defined returns format. Every sentence is informative and earns its place. No redundancy.

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 tool's complexity (5 parameters, 1 required, no enums, output schema present), the description is fully complete: it explains behavior, usage tips, parameter nuances, and the return structure. The output schema is described even though it's not required, adding clarity.

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 how multi-word keywords are handled (split into individual words that must all match), which is not in the schema. This extra context justifies a 4.

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 action ('Search for studies using flexible keyword matching across titles and descriptions') and distinguishes it from sibling tool search_rna_studies, making the purpose and differentiation explicit.

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?

Provides explicit when-to-use guidance ('Use this for broad exploratory searches when search_rna_studies() is too restrictive'), lists specific use cases, includes important notes on keyword splitting and search tips, and contrasts with alternatives.

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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