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Heroku MCP server

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by heroku

pg_ps

Monitor active database queries and processes on Heroku apps to identify long-running queries, track progress, debug performance issues, and view resource usage in real-time.

Instructions

Monitor active database queries and processes. Use this tool when you need to: 1) View currently executing queries, 2) Track query progress and resource usage, 3) Identify long-running or blocked queries, 4) Debug performance issues in real-time. The tool provides detailed visibility into database activity with optional verbose output.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appYesThe name of the Heroku app whose database processes to view.
databaseNoConfig var containing the connection string, unique name, ID, or alias of the database. To access another app's database, prepend the app name to the config var or alias with `APP_NAME::`. If omitted, DATABASE_URL is used.
verboseNoWhen true, shows additional query details including query plan and memory usage.
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 discloses the tool provides 'detailed visibility' and 'optional verbose output' for additional details, which helps understand its behavior. However, it doesn't mention potential rate limits, authentication requirements, or whether this is a read-only operation (though 'monitor' implies non-destructive).

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 efficiently structured with a clear purpose statement followed by a bulleted list of use cases and a concluding sentence about capabilities. Every sentence adds value with zero wasted words, making it easy to parse and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a monitoring tool with no annotations and no output schema, the description provides good context about what the tool does and when to use it. However, it doesn't describe the return format or what specific information is included in the output, which would be helpful given the lack of output schema.

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 description coverage is 100%, so the schema already fully documents all three parameters. The description mentions 'optional verbose output' which aligns with the 'verbose' parameter but doesn't add meaningful semantic context beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

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 with specific verbs ('monitor', 'view', 'track', 'identify', 'debug') and resources ('active database queries and processes'). It distinguishes from siblings like pg_kill (termination) and pg_info (static info) by focusing on real-time monitoring of executing queries.

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 explicitly lists four specific use cases when to use this tool: 1) View currently executing queries, 2) Track query progress and resource usage, 3) Identify long-running or blocked queries, and 4) Debug performance issues in real-time. This provides clear guidance on appropriate contexts 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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