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Glama

Fas

by io.github.pipeworx-io

Fas Exports

fas_exports
Read-onlyIdempotent

Check US agricultural exports by commodity and destination. Returns export volumes, values, and trade partner details. Use fas_commodity_codes to find commodity codes (e.g., "corn", "wheat").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryNoDestination country code (e.g., "CN" for China, "MX" for Mexico, "JP" for Japan). Optional — omit for all destinations.
end_yearNoEnd year (e.g., "2024"). Optional.
commodityYesCommodity name (e.g., "corn", "soybeans", "wheat", "beef", "pork", "cotton") or commodity code
start_yearNoStart year (e.g., "2020"). Optional.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesExport records (max 100 returned)
countYesNumber of records returned
commodityYesCommodity name provided in request
directionYesTrade direction
truncatedYesWhether results were truncated to 100 records
commodity_codeYesStandardized commodity code

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed7 schema fields changed
    • changedOutput schema / properties / commodity / description
      Previous value: -"Commodity name provided in the request"New value: +"Commodity name provided in request"
    • changedOutput schema / properties / commodity_code / description
      Previous value: -"FAS commodity code used for the query"New value: +"Standardized commodity code"
    • changedOutput schema / properties / count / description
      Previous value: -"Number of export records returned"New value: +"Number of records returned"
    • changedOutput schema / properties / data / description
      Previous value: -"Export records (up to 100)"New value: +"Export records (max 100 returned)"
    • changedOutput schema / properties / data / items / properties / partner / description
      Previous value: -"Destination country or partner name"New value: +"Destination country/partner name"
    • changedOutput schema / properties / data / items / properties / partner_code / description
      Previous value: -"Partner country code"New value: +"Destination country code"
    • changedOutput schema / properties / truncated / description
      Previous value: -"Whether more than 100 records exist"New value: +"Whether results were truncated to 100 records"
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "commodity": {
+      "description": "Commodity name provided in the request",
+      "type": "string"
+    },
+    "commodity_code": {
+      "description": "FAS commodity code used for the query",
+      "type": "string"
+    },
+    "count": {
+      "description": "Number of export records returned",
+      "type": "integer"
+    },
+    "data": {
+      "description": "Export records (up to 100)",
+      "items": {
+        "properties": {
+          "month": {
+            "description": "Export month",
+            "type": [
+              "integer",
+              "null"
+            ]
+          },
+          "partner": {
+            "description": "Destination country or partner name",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "partner_code": {
+            "description": "Partner country code",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "quantity": {
+            "description": "Export quantity",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "unit": {
+            "description": "Unit of measurement",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "value": {
+            "description": "Export value",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "year": {
+            "description": "Export year",
+            "type": [
+              "integer",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "direction": {
+      "description": "Trade direction",
+      "enum": [
+        "exports"
+      ],
+      "type": "string"
+    },
+    "truncated": {
+      "description": "Whether more than 100 records exist",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "commodity",
+    "commodity_code",
+    "direction",
+    "count",
+    "data",
+    "truncated"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "commodity": "corn",
+    "country": "CN",
+    "end_year": "2024",
+    "start_year": "2020"
+  },
+  {
+    "commodity": "soybeans"
+  }
+]
  4. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, which the description reinforces by stating it returns data. The description adds value by specifying the nature of the returned data (volumes, values, partner details) without contradicting 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?

The description is extremely concise: two sentences plus a useful hint, all front-loaded with the core purpose. No wasted words.

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 presence of an output schema, the description does not need to detail return fields. The parameter descriptions are complete, and the guidance for commodity codes is sufficient. The tool is well-contextualized among siblings.

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% with clear descriptions for all parameters. The description adds context beyond the schema by recommending use of the sister tool for codes, and examples illustrate usage. This elevates from baseline 3 to 4.

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 identifies the tool's purpose: checking US agricultural exports by commodity and destination, and returning volumes, values, and trade partner details. It uses a specific verb ('Check') and resource, and distinguishes itself from siblings like fas_imports and fas_commodity_codes.

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?

The description advises using fas_commodity_codes to find commodity codes, providing useful guidance for preparation. It implies when to use this tool (exports) versus alternatives (imports) but does not explicitly list exclusions or when not to use.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., ask_pipeworx vs. deep_research vs. validate_claim). However, a few pairs like ask_pipeworx_beta vs. ask_pipeworx and validate_claim vs. ask_pipeworx_grounded have overlapping roles, even though descriptions do differentiate them.

Naming Consistency4/5

All tool names use snake_case consistently, and many follow a verb_noun pattern (ask_pipeworx, compare_entities, subscribe). Some exceptions like entity_profile, recent_alerts, and pipeworx_trending break the strict verb_noun pattern but remain readable and stylistically uniform.

Tool Count3/5

With 35 tools, this is above the typical 'well-scoped' range and exceeds the 25-tool threshold for heavy servers. However, the server covers a very broad domain (financial data, prediction markets, agriculture, AI visibility, memory, subscriptions), which partially justifies the count, but it still feels bloated.

Completeness4/5

The tool surface covers all major workflows: data querying (ask_pipeworx, deep_research), entity resolution and comparison, prediction-market analysis, agricultural data (FAS tools), memory (remember/recall/forget), and subscription management (subscribe/unsubscribe/list). The only minor gap is lack of direct write/update operations for external data, but that's not expected for a read-heavy platform.

Resources