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Glama

Fas

by io.github.pipeworx-io

Fas Imports

fas_imports
Read-onlyIdempotent

Check US agricultural imports by commodity and origin country. Returns import volumes, values, and source country details. Use fas_commodity_codes to find commodity codes (e.g., "coffee", "cocoa").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryNoOrigin country code (e.g., "BR" for Brazil, "CO" for Colombia). Optional — omit for all origins.
end_yearNoEnd year (optional)
commodityYesCommodity name (e.g., "coffee", "cocoa", "sugar", "beef") or commodity code
start_yearNoStart year (optional)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesImport 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 import records returned"New value: +"Number of records returned"
    • changedOutput schema / properties / data / description
      Previous value: -"Import records (up to 100)"New value: +"Import records (max 100 returned)"
    • changedOutput schema / properties / data / items / properties / partner / description
      Previous value: -"Origin country or partner name"New value: +"Origin country/partner name"
    • changedOutput schema / properties / data / items / properties / partner_code / description
      Previous value: -"Partner country code"New value: +"Origin 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 import records returned",
+      "type": "integer"
+    },
+    "data": {
+      "description": "Import records (up to 100)",
+      "items": {
+        "properties": {
+          "month": {
+            "description": "Import month",
+            "type": [
+              "integer",
+              "null"
+            ]
+          },
+          "partner": {
+            "description": "Origin country or partner name",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "partner_code": {
+            "description": "Partner country code",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "quantity": {
+            "description": "Import quantity",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "unit": {
+            "description": "Unit of measurement",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "value": {
+            "description": "Import value",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "year": {
+            "description": "Import year",
+            "type": [
+              "integer",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "direction": {
+      "description": "Trade direction",
+      "enum": [
+        "imports"
+      ],
+      "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": "coffee",
+    "country": "BR",
+    "start_year": "2022"
+  },
+  {
+    "commodity": "cocoa"
+  }
+]
  4. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds that it returns import volumes, values, and source country details, offering some behavioral context 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?

Two sentences, front-loaded with purpose and output, zero waste. Every sentence earns its place.

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?

Output schema exists, so return values are covered. Description covers purpose, output type, and a cross-tool hint. For a simple query tool with 4 parameters and 1 required, it is fairly complete, though optional year parameters are not highlighted.

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?

Schema description coverage is 100%, so baseline is 3. The description adds no param-specific detail beyond the schema, but provides overall output context.

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 uses a specific verb ('Check') and resource ('US agricultural imports by commodity and origin country'), clearly distinguishing it from sibling tools like fas_exports and fas_production.

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?

Explicitly advises to use fas_commodity_codes to find commodity codes, guiding appropriate usage. Lacks explicit when-not-to-use comparisons with siblings, but provides clear context.

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