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Python
Hybrid

get_graph_list

Read-only

Get a CSV list of named graphs from a SPARQL endpoint, filtering out system graphs and prioritizing matching database names to identify relevant datasets.

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changedv2.0.0
    • removedInput schema / properties / database / default
      Removed value: -""
    • changedInput schema / properties / database / description
      Previous value: -"RDF database name (e.g. 'uniprot', 'chembl'). 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. Optional if `endpoint_url` or `endpoint_name` is provided."New value: +"RDF 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."
    • removedInput schema / properties / db
      Removed value: -{
-  "default": "",
-  "type": "string"
-}
    • removedInput schema / properties / dbname
      Removed value: -{
-  "default": "",
-  "type": "string"
-}
    • addedInput schema / required
      Added value: +[
+  "database"
+]
  2. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint and openWorldHint), the description details specific behaviors: internal graphs are filtered out, graph URIs containing the database substring are ranked first, and it returns a CSV list. It also discloses the error response prefix, adding transparency about output format and failure modes.

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 front-loaded with purpose, then proceeds to usage constraints, ranking behavior, fallback logic, and return format. Each sentence adds value without redundancy. Though a bit long, the density of relevant information justifies its length.

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 and a complex registry logic, the description covers all necessary context: required input, alternative inputs, ranking behavior, internal graph filtering, and error handling. The presence of an output schema is complemented by the description's explanation of CSV format and 'Error:' prefix, making the tool fully understandable.

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?

The schema descriptions already cover all parameters (100% coverage), but the description adds semantic depth: it explains the dual role of database (validation vs ranking), priority between endpoint_url and endpoint_name, and how include_system affects output. This goes beyond simple schema documentation and helps agents use parameters correctly.

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 a list of named graphs on a SPARQL endpoint.' It specifies the action (list), the resource (named graphs), and the context (SPARQL endpoint). It also distinguishes from siblings by highlighting endpoint discovery and filtering/ranking behavior, which is unique among the provided tools.

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 provides explicit usage guidance: 'ALWAYS pass database (required)' and explains when to use endpoint_url vs endpoint_name for unregistered databases, and how database value is used for ranking. It also warns about the 'Error:' prefix in return values, covering both usage conditions and error expectations.

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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