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Save a workflow

save_workflow

Save a re-runnable, parameterized workflow: an ordered list of natural-language instructions (step 1 builds, later steps mutate). Wrap values in {braces} and declare them in params. Running it later re-executes the instructions on FRESH data and bills per step like typing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesWorkflow name.
stepsYesOrdered NL instructions. Step 1 builds the map; later steps mutate it. Wrap values in {braces} to declare parameters.
paramsNoDeclared parameters — every {slot} used in steps must be declared here.
rationaleYesShort audit-log label (≤300 chars) stating the user-facing goal this call serves, e.g. "add wildfire layer for the user's California query". Required on every call. Stored in the operations log so map edits stay traceable — we never see your chat history.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only carry negative hints, so the description carries the burden. It discloses several important behaviors: execution happens later on FRESH data, billing is per step like typing, and {braces} values must be declared in params. This is meaningful context beyond the schema and annotations.

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 front-loaded sentences with no filler. Each sentence contributes a distinct fact: reusability, structure/parameterization, and later execution behavior with billing. Nothing is redundant.

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?

The description, combined with the schema, gives an agent what it needs: purpose, execution semantics, param handling, and the required rationale field. A minor gap is that it doesn't address return values or overwrite behavior when saving an existing workflow name, but this is not critical for correct invocation.

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?

Schemas cover 100% of parameters, giving a baseline of 3. The description adds value by explaining the relationship between steps and params via {braces} declarations and the step 1 build/later mutate pattern, which is not obvious from the schema alone.

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 saves a re-runnable, parameterized workflow—an ordered list of NL instructions with step 1 building and later steps mutating. This specific verb+resource framing distinguishes it from siblings like run_workflow and list_workflows without needing to open the schema.

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 clear context: use this to persist a reusable workflow, and running it later re-executes instructions on fresh data and bills per step. It doesn't explicitly name alternatives or state when not to use it, so it stops short of a 5.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but a few pairs such as select_within/focus_area and correlate_layers/add_fusion_layer overlap in function, differing mainly by input type or output (read vs. write). The descriptions adequately explain these differences, so agents can distinguish them with careful reading.

Naming Consistency5/5

Tool names consistently follow a verb_noun snake_case pattern (add_layer, filter_layer, export_image, remove_annotation). Minor exceptions like undo/redo/tag are conventional single verbs and do not detract from the overall predictability.

Tool Count4/5

At 25 tools, the server is on the heavy side, but each tool serves a distinct operation in a comprehensive mapping workspace—covering creation, editing, analysis, export, and history. The number feels justified by the broad feature set rather than excessive.

Completeness4/5

The toolset covers the full lifecycle of layers and workspace state, including add/remove/rename/restyle/filter, build/get/mutate/undo/redo, export, and tagging. Minor gaps such as annotation editing, layer reordering, or direct data updates exist, but they are not critical blockers for typical workflows.

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