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MCPg - Production-grade PostgreSQL MCP Server

Get PGQ status

get_pgq_status
Read-only

Check whether SQL/PGQ property graph queries are usable on the PostgreSQL server, guiding the choice between PGQ and Cypher-based graph operations.

Instructions

Report whether SQL/PGQ (the SQL standard for property graph queries, new in PG 19) is usable on this server. SQL/PGQ coexists with the AGE-style graph_operations bucket — get_pgq_status is the agent's hint about which surface to reach for. Never raises; on PG < 19 reports available=false with a diagnostic pointing at run_cypher. Returns an object with available (bool), server_version_num (int), server_version (the human-readable string), and detail (a guidance string the agent can surface to the user).

Example: get_pgq_status()

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
databaseNoOptional: target a configured secondary (read-only) database by name; omit for the primary. Call list_databases to see the configured ids.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailYes
availableYes
server_versionYes
server_version_numYes
Behavior4/5

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

The description adds value beyond the readOnlyHint annotation by detailing that it never raises, returns a structured object with specific fields, and describes version-dependent behavior (available=false for PG < 19).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with the main purpose first, followed by behavioral details, return format, and an example. It is concise but could be slightly tighter, though it earns 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?

Given the tool's simplicity and the presence of an output schema, the description covers all necessary aspects: purpose, behavior, error handling, return values, and usage context. It is fully complete.

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?

The schema covers the single optional parameter (database) completely. The description does not add any additional meaning about the parameter beyond what the schema already 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 it reports whether SQL/PGQ is usable on the server, differentiating it from related tools like run_cypher and run_pgq by explaining it's a hint about which graph surface to use.

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 implies it's a precursor to using graph query tools (run_cypher for PG<19, run_pgq otherwise) and includes a diagnostic pointing to run_cypher when unavailable. It provides clear context but lacks explicit when-not-to-use statements.

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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