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Glama

Add a layer (direct)

add_layer

Append a fully-constructed LayerPlan to an existing workspace without going through NL. Pass the same shape the workspace pipeline emits — { id, title, source, viz, geometry, ... }. Prefer this when you already have a structured plan; otherwise use mutate_map. For the full LayerPlan and Op spec, fetch the MCP resource showmeonmap://docs/workspace-ops-spec.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentNoOptional. Which mental model is this add_layer operating under? "primary-data" = the layer the user actually asked to see; "context-overlay" = supporting data layered on top of a primary; "reference-boundary" = admin/geo boundary used as a backdrop; "annotation" = caller-placed callout / pin layer; "comparison" = a layer added to compare against another; "other" = none of the above.
map_idYesmap_id from a prior build_map call
layerPlanYesFull LayerPlan object. Must include id, title, source, viz, geometry.
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
Behavior4/5

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

The description adds valuable behavioral context beyond annotations: it explains that the tool appends without NL, describes the expected shape of the LayerPlan, and notes the rationale parameter's purpose. No contradiction with annotations (readOnlyHint=false, etc.). However, it could briefly mention the outcome of appending (e.g., layer added to workspace).

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 three sentences, each with a clear purpose: first states the action, second provides the LayerPlan shape, third gives usage guidance and a doc reference. No waste, front-loaded with key info.

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?

Given the parameters and lack of output schema, the description covers the purpose, usage, and parameter hints. It references external docs for the full spec, which is appropriate. Could be slightly more explicit about what happens upon success (e.g., layer added to workspace), but overall complete for an agent.

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 baseline is 3. The description adds extra meaning by specifying the LayerPlan shape ('same shape the workspace pipeline emits — { id, title, source, viz, geometry, ... }') and clarifying rationale as a 'short audit-log label'. This enhances understanding beyond 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 action ('Append a fully-constructed LayerPlan to an existing workspace'), specifies the resource (LayerPlan to workspace), and distinguishes it from sibling tools like mutate_map by noting it bypasses NL. The verb and target are specific and unambiguous.

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

Usage Guidelines5/5

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

The description explicitly advises when to use this tool ('when you already have a structured plan') and when to use an alternative ('otherwise use mutate_map'). It also references external documentation for the full spec, providing clear guidance for the agent.

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.

Resources