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biocontext-ai

nucleotide_archive_mcp

get_available_fields

Discover available search and return fields for ENA data types to enable custom query building.

Instructions

Get available search and return fields for an ENA result type.

Usage Tips

Use to discover what fields you can search on and what metadata fields are available for a given data type in ENA. Helpful for building custom queries with build_custom_query().

Returns

dict Dictionary containing: - result_type: The queried result type - search_fields: List of searchable fields with id, description, type (if requested) - search_fields_count: Number of search fields (if requested) - return_fields: List of returnable fields with id, description, type (if requested) - return_fields_count: Number of return fields (if requested) - error: Error message if any

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
result_typeNoType of ENA data to query (read_study, study, sample, read_run, read_experiment, analysis)read_study
field_categoryNoWhich fields to return (all, search, return)all

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries full burden. It describes the return structure but does not explicitly mention that the tool is read-only or has no side effects. Additional transparency about safety or constraints would improve this.

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 well-structured with sections, front-loaded with the main purpose, and concise. Every sentence adds value without unnecessary repetition.

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 (2 parameters, detailed output), the description comprehensively covers purpose, usage, and return structure. No obvious gaps are present.

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 coverage is 100% and descriptions are already in the schema. The description adds context about using the results for custom queries, but does not add significant semantic meaning beyond what the schema provides.

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: 'Get available search and return fields for an ENA result type.' This is a specific verb and resource, and it distinguishes from sibling tools like build_custom_query which uses these fields.

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

Usage Guidelines4/5

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

The description includes usage tips that explicitly mention using this before build_custom_query, providing context for when to use it. However, it does not state when not to use it or list alternative tools, so it's slightly incomplete.

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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