list_workflowsList the caller's saved workflows with their parameters and per-run credit estimates.
| Name | Required | Description | Default |
|---|---|---|---|
| rationale | Yes | Short 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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations mark readOnlyHint as false, so this is not presented as a side-effect-free call, and the description adds output scope ('parameters and per-run credit estimates'). However, the description does not disclose the audit-log side effect suggested by the rationale parameter, so the agent must infer behavioral nuance from the schema rather than the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence conveys the resource, the scope, and the return value without wasted words. Every element contributes to understanding what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the return content well, and the schema handles the required rationale parameter. It is slightly light on behavioral context, but for a simple list-style tool with strong schema coverage, nothing essential is missing for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the rationale parameter is fully documented in the schema. The description does not need to repeat it, but it also adds no parameter-level meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List'), a precise resource ('the caller's saved workflows'), and the useful output details ('parameters and per-run credit estimates'). This clearly distinguishes it from siblings like run_workflow and save_workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The listing purpose is implied: an agent would call this to inspect existing workflows before running or managing them. However, there is no explicit guidance about when to choose this over alternatives, no exclusions, and no mention of prerequisite conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.