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OBS MCP Server

OBS MCP Server

by royshil
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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obs-get-input-list

Retrieve a list of all OBS inputs, optionally filtered by type, to manage and configure sources in your streaming setup.

Instructions

Gets an array of all inputs in OBS

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "obs-get-input-list"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputKindNoRestrict the array to only inputs of the specified kind

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.0
    • addedInput schema / $schema
      Added value: +"http://json-schema.org/draft-07/schema#"
    • addedInput schema / additionalProperties
      Added value: +false
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden but only states it retrieves an array without detailing behavioral traits like permissions, rate limits, or output format. It fails to disclose whether this is a read-only operation, if it requires authentication, or how errors are handled, which are critical for safe invocation.

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 a single, direct sentence with no wasted words, front-loading the core action and resource. It efficiently communicates the tool's purpose without unnecessary elaboration, 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.

Completeness2/5

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

Given no annotations and no output schema, the description is insufficient for a tool that retrieves data. It lacks details on return values, error conditions, or behavioral constraints, leaving gaps that could hinder an AI agent's ability to use the tool effectively in complex scenarios.

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 input schema fully documents the optional 'inputKind' parameter. The description adds no additional semantic context beyond implying retrieval of inputs, which aligns with the schema but does not enhance understanding. This meets the baseline for high schema coverage.

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 the action ('Gets') and resource ('array of all inputs in OBS'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'obs-get-input-kind-list' or 'obs-get-input-settings', which reduces clarity in distinguishing exact use cases among similar retrieval tools.

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?

No guidance is provided on when to use this tool versus alternatives, such as filtering inputs by kind or accessing specific input details. The description lacks context on prerequisites, exclusions, or comparisons to sibling tools, leaving usage decisions ambiguous for an AI agent.

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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