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kmaneesh

BioPython MCP Server

by kmaneesh

entrez_link

Find related records across NCBI databases by linking source IDs to target databases, such as discovering PubMed articles for a gene or nucleotide sequences for a protein.

Instructions

Find related records across NCBI databases using ELink.

This tool discovers relationships between records in different databases, such as finding PubMed articles related to genes, or nucleotide sequences related to proteins.

Args: source_db: Source database (e.g., 'gene', 'protein', 'clinvar') target_db: Target database to link to (e.g., 'pubmed', 'nucleotide') ids: Single ID, comma-separated string, or list of IDs from source_db link_name: Specific link type (optional, empty = all available links)

Returns: Dictionary containing: - source_db: Source database name - target_db: Target database name - source_ids: List of source IDs queried - linked_ids: Dict mapping source IDs to lists of linked target IDs - total_links: Total number of links found - link_name: Link type used (if specified)

Examples: >>> entrez_link("gene", "pubmed", "672") # BRCA1 gene to PubMed >>> entrez_link("protein", "nucleotide", ["NP_000198.1", "NP_001121"]) >>> entrez_link("clinvar", "pubmed", "12345", link_name="clinvar_pubmed")

Notes: - Discovers cross-database relationships automatically - Use entrez_info() to see available link names for databases - Rate limited (3 req/sec or 10 req/sec with API key) - Different databases support different link types

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_dbYes
target_dbYes
idsYes
link_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It discloses rate limits (3/10 req/sec), that different databases support different link types, and notes that relationships are discovered automatically. It could mention that the operation is read-only and behavior on invalid IDs, but overall sufficiently transparent.

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 Args, Returns, Examples, and Notes sections. Each sentence adds value, no fluff. The purpose is front-loaded, and the structure helps an agent quickly parse key information.

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 (cross-database linking), the description covers input parameters, returns (dictionary fields documented), usage guidance (rate limits, link name discovery), and examples. With an output schema present, the return documentation is sufficient.

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

Parameters5/5

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

Schema coverage is 0% (no descriptions in input schema), but the description extensively documents each parameter in the Args section, including types and examples. The ids parameter's flexibility (single ID, comma-separated, list) is clarified. Examples show typical usage.

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 that the tool finds related records across NCBI databases, with specific verb ('find related records') and resource ('across NCBI databases'). Examples like 'BRCA1 gene to PubMed' and mention of different databases distinguish it from sibling tools like entrez_fetch or entrez_search.

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?

Provides context on when to use (discovering cross-database relationships) and notes to use entrez_info for available link names. However, it does not explicitly state when not to use it or mention alternatives like entrez_search for direct queries.

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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