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get_graph_list

List named graphs on a SPARQL endpoint, filtering out system graphs, and ranking database-specific graphs first. Works with registered and unregistered databases.

Instructions

Get a list of named graphs on a SPARQL endpoint. ALWAYS pass database (required). Virtuoso/OpenLink internal graphs are filtered out. Graph URIs containing the database substring (case-insensitive) are ranked first — useful when the endpoint hosts multiple databases (e.g. SIB hosts UniProt + Rhea + Bgee + OMA). For a database not yet in the registry, pass endpoint_url (or endpoint_name if its parent endpoint is registered) to bypass database validation; the required database value is then used only as a ranking hint. RETURNS a CSV-formatted list of named graphs (database-name matches first); on missing endpoint selection it returns a string beginning with 'Error:' — check for that prefix before use.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
databaseYesRDF database name (e.g. 'uniprot', 'chembl'). Required. When the name is in the registry it resolves the endpoint URL; in any case the value is used as a case-insensitive substring to rank matching graph URIs first. For an unregistered database, also pass `endpoint_url` or `endpoint_name` (which take priority); `database` is then just the ranking hint.
endpoint_urlNoDirect SPARQL endpoint URL. Use when neither the database nor its parent endpoint name is in the registry.
endpoint_nameNoShort endpoint name (e.g. 'primary', 'sib', 'ebi'). Use when the database is not yet registered but its parent endpoint is.
include_systemNoIf True, include Virtuoso/OpenLink internal graphs (virtrdf, ldp, activitystreams, etc.). Default False — these are never useful for queries.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: filtering of internal graphs, ranking by database substring, return format (CSV), and error behavior (string starting with 'Error:'). This provides comprehensive 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?

The description is concise (5 sentences) and well-structured: purpose first, then requirement, exceptions, return type. Every sentence adds essential information without 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?

For a tool with 4 parameters, one required, and an output schema, the description covers usage, error handling, return format, and edge cases (unregistered databases). It is fully complete.

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 the ranking hint, fallback logic, and the purpose of include_system, surpassing the schema detail.

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 starts with a clear verb+resource: 'Get a list of named graphs on a SPARQL endpoint.' It further distinguishes the tool by detailing filtering and ranking logic, making it distinct from siblings like run_sparql or search tools.

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 explicitly requires 'database' and explains when to use endpoint_url or endpoint_name for unregistered databases. It advises checking for 'Error:' prefix. While it does not contrast with siblings, the context is clear for tool selection.

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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