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usgs-water-mcp-server

by io.github.cyanheads
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Water Dataframe Describe

water_dataframe_describe
Read-onlyIdempotent

List tables and columns staged on a DataCanvas by water_get_series or water_find_sites. Call this after water_get_series or water_find_sites returns a canvas_id to discover the exact table name and column types before writing a query. Then pass the table name to water_dataframe_query. Requires DataCanvas to be enabled on this server instance. Returns an error if DataCanvas is not available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
canvas_idYesCanvas ID returned by water_get_series or water_find_sites. Identifies the canvas to describe.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
tablesNoTables and views on this canvas.
canvas_idNoThe canvas ID that was described — pass to water_dataframe_query.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / canvas_id / pattern
      Added value: +"^[A-Za-z0-9_-]{10}$"
  2. Changed6 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedInput schema / additionalProperties
      Added value: +false
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedOutput schema / anyOf
      Added value: +[
+  {
+    "not": {
+      "required": [
+        "error"
+      ]
+    },
+    "required": [
+      "tables",
+      "canvas_id"
+    ]
+  },
+  {
+    "required": [
+      "error"
+    ]
+  }
+]
    • addedOutput schema / properties / error
      Added value: +{
+  "additionalProperties": {},
+  "description": "Present when the call failed. Absent on success.",
+  "properties": {
+    "code": {
+      "description": "JSON-RPC error code for this failure.",
+      "maximum": 9007199254740991,
+      "minimum": -9007199254740991,
+      "type": "integer"
+    },
+    "data": {
+      "additionalProperties": {},
+      "properties": {
+        "reason": {
+          "description": "Machine-readable failure mode. Declared by this tool: `canvas_disabled`: DataCanvas is not enabled on this server instance. `canvas_not_found`: The canvas_id does not exist or has expired. Other values are possible when a failure originates below the handler.",
+          "examples": [
+            "canvas_disabled",
+            "canvas_not_found"
+          ],
+          "type": "string"
+        },
+        "recovery": {
+          "additionalProperties": {},
+          "description": "Actionable next step for the caller.",
+          "properties": {
+            "hint": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "hint"
+          ],
+          "type": "object"
+        },
+        "retryable": {
+          "description": "Whether retrying may succeed.",
+          "type": "boolean"
+        }
+      },
+      "type": "object"
+    },
+    "message": {
+      "description": "Human-readable description of what went wrong.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "code",
+    "message"
+  ],
+  "type": "object"
+}
    • removedOutput schema / required
      Removed value: -[
-  "tables",
-  "canvas_id"
-]
  3. Changed1 schema field changed
    • changedInput schema / properties / canvas_id / description
      Previous value: -"Canvas ID returned by water_get_series. Identifies the canvas to describe."New value: +"Canvas ID returned by water_get_series or water_find_sites. Identifies the canvas to describe."
  4. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds meaningful context about a prerequisite: DataCanvas must be enabled, with an error returned otherwise. It could go slightly deeper into output behavior, but the output schema covers that, so this is strong.

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 compact sentences deliver purpose, position in the workflow, and a prerequisite/error condition with no filler. The most important routing information is front-loaded.

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?

Given the output schema covers return shape, the description fully addresses what the agent needs: why to call it, when to call it, what to do next, and one operational requirement. Nothing important is missing for correct invocation.

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

Parameters3/5

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

The single canvas_id parameter is fully documented by the schema, which already explains it is returned by water_get_series or water_find_sites. The description reinforces this workflow but adds no new semantic detail beyond 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 names a specific verb and resource: listing tables and columns staged on a DataCanvas. It clearly distinguishes this tool from water_dataframe_query by framing it as the discovery/description step before writing a query.

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?

It gives an explicit workflow: call after water_get_series or water_find_sites returns a canvas_id, use it to learn the table name and column types, then pass that table name to water_dataframe_query. This provides clear when-to-use guidance and even names the downstream alternative.

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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