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Fas

by io.github.pipeworx-io

Fas Commodity Codes

fas_commodity_codes
Read-onlyIdempotent

Search agricultural commodity codes and names. Returns commodity IDs, descriptions, and categories. Use results with fas_production, fas_exports, and fas_imports.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNoSearch keyword (e.g., "soy", "wheat"). Optional.
categoryNoFilter by category: "Grains", "Oilseeds", "Meat", "Dairy", "Fiber", "Sugar", "Tropical" (optional)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYesUsage guidance for commodity and country codes
totalYesTotal number of matching commodity codes
categoriesYesCommodities grouped by category
country_codesYesMapping of ISO 2-letter country codes to country names

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedOutput schema / properties / country_codes / description
      Previous value: -"ISO 2-letter country codes and names"New value: +"Mapping of ISO 2-letter country codes to country names"
    • changedOutput schema / properties / note / description
      Previous value: -"Usage note for the codes"New value: +"Usage guidance for commodity and country codes"
    • changedOutput schema / properties / total / description
      Previous value: -"Total number of commodities matching filter"New value: +"Total number of matching commodity codes"
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "categories": {
+      "additionalProperties": {
+        "items": {
+          "properties": {
+            "code": {
+              "description": "Commodity code",
+              "type": "string"
+            },
+            "name": {
+              "description": "Commodity name",
+              "type": "string"
+            }
+          },
+          "required": [
+            "code",
+            "name"
+          ],
+          "type": "object"
+        },
+        "type": "array"
+      },
+      "description": "Commodities grouped by category",
+      "type": "object"
+    },
+    "country_codes": {
+      "additionalProperties": {
+        "type": "string"
+      },
+      "description": "ISO 2-letter country codes and names",
+      "type": "object"
+    },
+    "note": {
+      "description": "Usage note for the codes",
+      "type": "string"
+    },
+    "total": {
+      "description": "Total number of commodities matching filter",
+      "type": "integer"
+    }
+  },
+  "required": [
+    "total",
+    "categories",
+    "country_codes",
+    "note"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "category": "Oilseeds",
+    "search": "soy"
+  },
+  {
+    "category": "Grains"
+  }
+]
  4. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds that it returns commodity IDs, descriptions, and categories, and that it is used with other tools, but no additional behavioral traits beyond annotations. Moderate added value.

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 two sentences long, front-loaded with the primary action, and contains no unnecessary words. Every sentence earns its place.

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 appropriately covers the purpose and return fields. It also provides context on how to use results with related tools. Complete for a search tool with annotations.

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 coverage is 100% with descriptions for both parameters (search and category). The description does not add further meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

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 tool searches agricultural commodity codes and names, returning IDs, descriptions, and categories. It distinguishes itself from sibling tools by mentioning integration with fas_production, fas_exports, and fas_imports.

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 explicitly states that results should be used with fas_production, fas_exports, and fas_imports, giving clear context on when to use this tool. It does not mention alternatives or when not to use it, but the guidance is sufficient.

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