Skip to main content
Glama

Run a saved workflow

run_workflow

Execute a saved workflow with parameter values. PAID: bills ~1 credit per step through the normal build/mutate paths (the estimate is in list_workflows). Optional batch mode sweeps ONE param across 2–20 values — each value is a full independent run (steps × values credits); failed/paused values are reported per value and the batch continues. A clarification pauses a single run (status "paused" + the question); failures stop at the failing step. On success the result is a live map with a shareable URL (batch: the last successful value's map).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batchNoBatch mode: run the workflow once per value ("each of the 12 districts"). Bills steps × values.
paramsNoValues for the workflow's declared parameters.
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.
workflow_idYesFrom save_workflow or list_workflows.

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the sparse annotations by disclosing that the operation is paid, that batch runs are independent and continue past failed/paused values, that clarifications pause a run, and that failures stop at the failing step. It also states the success outcome—a live map with a shareable URL—which would otherwise be invisible without an output schema.

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 dense but efficient: every sentence adds a distinct fact (purpose, cost, batch behavior, failure handling, result format). The purpose is front-loaded, and no sentence is redundant padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

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

For a tool with no output schema, it fully explains the result shape, cost implications, batch semantics, and failure modes. Given the 100% parameter schema coverage, the description plus schema leaves an agent with everything needed to invoke 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?

Schema coverage is 100%, so the schema already documents every parameter. The description adds meaningful semantics for batch mode ('each value is a full independent run,' credit multiplication, per-value reporting) and explains what workflow_id implies via list_workflows, though most parameter syntax remains in the schema.

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 opens with a specific verb and resource—'Execute a saved workflow with parameter values'—and distinguishes itself from siblings like save_workflow by focusing on execution rather than creation or listing. It also clarifies batch mode as a distinct execution variant, so the tool's role is unambiguous.

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 clearly describes the normal execution path and the optional batch mode, including when batch mode applies (sweeping one parameter across 2-20 values) and how credits are billed. It does not explicitly enumerate exclusions or alternatives like 'use save_workflow to create,' but the usage context is clear from 'execute a saved workflow' and the reference to list_workflows for estimates.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources