Skip to main content
Glama
Sign In
krita-mcp

krita-mcp

by jowinjohnchemban
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

batch

Run up to 200 Krita MCP tool calls sequentially in one request to avoid step-by-step overhead; get one result per operation, with images returned after the summary.

Instructions

Run several tool calls in one go, in order: [{"tool": "draw_shapes", "args": {...}}, ...] (max 200). Any tool except batch itself. Returns one result per operation; images (canvas_snapshot, view_image) are returned after the summary. Much faster than one call per step.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "operations": "<operations>"
    },
    "name": "batch"
  }
}

Input Schema

NameRequiredDescriptionDefault
operationsYes
stop_on_errorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.6/5.0
Behavior2/5

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

No annotations exist, so the description carries the full behavioral burden. It does disclose ordering, the 200 cap, non-recursiveness, and that images come back after the summary, but it says nothing about failure semantics: whether a failing operation aborts the remainder, whether completed operations persist, or whether any of this is destructive or reversible. For a tool that can execute up to 200 mutating calls, that is a major omission.

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?

Three dense sentences, no filler: capability, payload format with a concrete example, then return behavior and the performance rationale. Front-loaded and sized well for a tool whose schema is nearly empty.

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?

With no output schema and no annotations, the description does cover return shape ('one result per operation', images after the summary) and the nesting restriction. It falls short on error handling and on 'stop_on_error', which is exactly the information an agent needs to decide whether a long batch is safe.

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 0%, yet the description fully explains the shape of 'operations' via the inline example (tool name plus args object) and its bound. The second parameter, 'stop_on_error', is never mentioned in the description, and the JSON schema carries only a title and default true — so its behavior on partial failure is undocumented in both places.

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?

States a specific verb and resource ('Run several tool calls in one go, in order'), shows the literal argument shape with an example, caps it at 200, and excludes the recursive case ('Any tool except batch itself'). An agent immediately knows this is a meta-dispatch tool distinct from every single-purpose sibling.

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

Usage Guidelines3/5

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

It implies usage conditions ('Much faster than one call per step') and states the one hard exclusion, but never says when batching is wrong — notably whether later calls can depend on earlier results, or whether the agent should fall back to individual calls when it needs intermediate output. That missing negative guidance matters for an orchestrator tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools