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nkarasiak

QGIS MCP

by nkarasiak
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Execute Processing Batch

execute_processing_batch

Run a QGIS processing algorithm repeatedly with different parameter sets in one batch, returning per-run success/error status. Apply the same operation to many inputs without multiple round-trips.

Instructions

Run one algorithm once per parameter dict in 'parameters_list'. Returns a per-run result with index and success/error status. Use for applying the same operation over many inputs in a single round-trip. timeout: seconds for the whole batch (default 55); runs that would start after it has elapsed come back as 'skipped' with the completed ones intact, so raise it or split the list for big batches. ellipsoid: measurement ellipsoid for every run (e.g. 'EPSG:7030'); default is the project's.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timeoutNo
algorithmYes
ellipsoidNo
parameters_listYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.15.0
    • addedInput schema / properties / ellipsoid
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
  2. Changed1 schema field changedv0.14.0
    • addedInput schema / properties / timeout
      Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
  3. Addedv0.5.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description fully covers behavioral details: it explains the timeout behavior in detail, stating that runs starting after the timeout are 'skipped' while completed ones remain intact, and it specifies the default and effect of the ellipsoid parameter. This goes beyond a generic description and gives the agent actionable knowledge about edge cases.

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 compact and well-structured: the first sentence establishes the core function, the second gives the usage context, and parameter explanations are appended concisely. Every sentence adds value, and key details like timeout behavior are front-loaded after the core purpose.

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 batch processing tool with no output schema and no annotations, the description covers the essential operational aspects: result format, timeout behavior, and parameter defaults. It could also mention whether runs are executed sequentially or if partial results are returned beyond the 'skipped' note, but it is sufficiently complete for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Since schema coverage is 0%, the description compensates well by explaining timeout (unit, default, behavior), ellipsoid (example, default), and parameters_list (list of dicts per run). It does not explicitly describe the algorithm parameter, but the context 'Run one algorithm' makes it clear, and the name itself is self-explanatory. Some additional detail about parameters_list structure could be beneficial, but it is adequate.

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 tool's function: 'Run one algorithm once per parameter dict in parameters_list' and mentions the per-run result format. It is specific about the batch behavior, but does not explicitly distinguish it from sibling tools like execute_processing or start_processing_job, leaving the differentiation implicit.

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?

It provides a clear use case: 'Use for applying the same operation over many inputs in a single round-trip.' This indicates when the tool is appropriate, but it does not mention alternatives or when not to use it, such as for asynchronous execution or single-run scenarios.

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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