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OpenSIPS MCP Server

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by OpenSIPS

observability_generate_prometheus_scrape

Generate a Prometheus scrape configuration for OpenSIPS metrics monitoring. Specify deployment name, host, port, metrics path, scrape interval, and optional multi-instance targets to collect metrics from your OpenSIPS servers.

Instructions

Generate a Prometheus scrape configuration.

Parameters

deployment_name: Used as the instance label so dashboards can pivot on it. opensips_host, prom_port: Default target when targets is omitted. metrics_path: Path the prometheus.so module serves on (default /prometheus). scrape_interval: Prometheus scrape interval. Default 15s. targets: Optional list of [label, "host:port"] pairs for multi-instance scraping. When omitted, a single target is generated from opensips_host + prom_port.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deployment_nameNoopensips
opensips_hostNoopensips
prom_portNo
metrics_pathNo/prometheus
scrape_intervalNo15s
targetsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description carries full burden. It states it generates a scrape configuration but does not disclose output format, return behavior, side effects, or whether it creates files. The existence of an output schema mitigates this slightly, but the description alone is insufficient for behavioral clarity.

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 a clear first sentence and organized parameter list. It is concise but could be slightly more compact by avoiding the docstring format. Slightly verbose for the focused task.

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?

Given the 6 parameters and no annotations, the description provides a coherent understanding of the tool's operation, especially the target generation logic. It does not describe the output structure, but the output schema likely covers that. Overall, it is nearly complete for an agent to use effectively.

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 description coverage is 0%, yet the description adds substantial meaning: deploment_name's role as instance label, default target logic, metrics_path default, and targets list behavior. This goes well beyond what the schema provides, fully compensating for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Generate a Prometheus scrape configuration', which is a specific verb and resource. However, it does not explicitly differentiate from sibling observability tools like 'observability_inject_prometheus' or 'observability_generate_bundle', which could have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains parameter roles but provides no guidance on when to use this tool versus alternatives, no prerequisites, and no use-case scenarios. It is purely parameter-focused without contextual usage advice.

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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