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Glama

Fas

by io.github.pipeworx-io

Fas Production

fas_production
Read-onlyIdempotent

Get global agricultural production, consumption, and inventory data by commodity and country. Returns production volumes, supply estimates, consumption figures, and trade flows by year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryNoCountry code (e.g., "US", "BR", "CN"). Optional — omit for world totals.
commodityYesCommodity name (e.g., "corn", "soybeans", "wheat") or PSD commodity code (e.g., "0440000")
market_yearNoMarket year (e.g., "2024"). Optional.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesProduction/supply/distribution records (max 100 returned)
typeYesData type
countYesNumber of records returned
commodityYesCommodity name provided in request
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. Changed5 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: -"PSD commodity code used for the query"New value: +"Standardized commodity code"
    • changedOutput schema / properties / count / description
      Previous value: -"Number of production records returned"New value: +"Number of records returned"
    • changedOutput schema / properties / data / description
      Previous value: -"Production/supply/distribution records (up to 100)"New value: +"Production/supply/distribution records (max 100 returned)"
    • 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": "PSD commodity code used for the query",
+      "type": "string"
+    },
+    "count": {
+      "description": "Number of production records returned",
+      "type": "integer"
+    },
+    "data": {
+      "description": "Production/supply/distribution records (up to 100)",
+      "items": {
+        "properties": {
+          "attribute": {
+            "description": "Attribute (e.g., production, consumption, inventory)",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "country": {
+            "description": "Country name",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "country_code": {
+            "description": "Country code",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "market_year": {
+            "description": "Market year",
+            "type": [
+              "integer",
+              "null"
+            ]
+          },
+          "unit": {
+            "description": "Unit of measurement",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "value": {
+            "description": "Attribute value",
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "truncated": {
+      "description": "Whether more than 100 records exist",
+      "type": "boolean"
+    },
+    "type": {
+      "description": "Data type",
+      "enum": [
+        "production_supply_distribution"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "commodity",
+    "commodity_code",
+    "type",
+    "count",
+    "data",
+    "truncated"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "commodity": "corn",
+    "country": "US",
+    "market_year": "2024"
+  },
+  {
+    "commodity": "wheat"
+  }
+]
  4. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds value by enumerating the types of data returned (production, consumption, inventory, trade flows), which helps the agent understand the output. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that packs relevant information. It is front-loaded with the verb 'Get' and the resource. Could be slightly more structured but is acceptable.

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 presence of an output schema (though not shown) and comprehensive annotations, the description covers the essential return fields and main usage. It does not explain behavior for multiple years or pagination, but it is mostly complete for a data retrieval tool.

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 the schema already documents all parameters well. The description adds that the tool returns data 'by commodity and country' and implies country is optional for world totals, but this is already in the schema. Little additional meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns production, consumption, inventory, and trade flows by commodity and country. It is specific about the data retrieved. However, it does not explicitly differentiate from sibling tools like fas_exports, which also cover trade flows, so clarity could be improved.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives such as fas_exports or fas_imports. The description does not mention prerequisites, exclusions, or context for appropriate 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