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Glama

Nhtsa

by io.github.pipeworx-io

Decode Vin

decode_vin
Read-onlyIdempotent

Decode a VIN to get vehicle details. Returns make, model, year, body style, engine type, drivetrain, and plant. E.g., '1HGBH41JXMN109186'. (For recalls/ratings use get_recalls / get_safety_ratings.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYes17-character VIN (e.g., "1HGBH41JXMN109186")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
vinYesVIN in uppercase
makeYesVehicle make/manufacturer name
trimYesVehicle trim level
doorsYesNumber of doors
modelYesVehicle model name
body_classYesBody class/style
drive_typeYesDrive type (AWD, FWD, RWD, etc.)
model_yearYesModel year
manufacturerYesManufacturer name
transmissionYesTransmission style
vehicle_typeYesType of vehicle
plant_countryYesCountry where vehicle was manufactured
all_attributesYesAll decoded attributes as key-value pairs
engine_cylindersYesNumber of engine cylinders
fuel_type_primaryYesPrimary fuel type
engine_displacement_lYesEngine displacement in liters

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "all_attributes": {
+      "additionalProperties": {
+        "type": "string"
+      },
+      "description": "All decoded attributes as key-value pairs",
+      "type": "object"
+    },
+    "body_class": {
+      "description": "Body class/style",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "doors": {
+      "description": "Number of doors",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "drive_type": {
+      "description": "Drive type (AWD, FWD, RWD, etc.)",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "engine_cylinders": {
+      "description": "Number of engine cylinders",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "engine_displacement_l": {
+      "description": "Engine displacement in liters",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "fuel_type_primary": {
+      "description": "Primary fuel type",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "make": {
+      "description": "Vehicle make/manufacturer name",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "manufacturer": {
+      "description": "Manufacturer name",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "model": {
+      "description": "Vehicle model name",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "model_year": {
+      "description": "Model year",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "plant_country": {
+      "description": "Country where vehicle was manufactured",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "transmission": {
+      "description": "Transmission style",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "trim": {
+      "description": "Vehicle trim level",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "vehicle_type": {
+      "description": "Type of vehicle",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "vin": {
+      "description": "VIN in uppercase",
+      "type": "string"
+    }
+  },
+  "required": [
+    "vin",
+    "make",
+    "model",
+    "model_year",
+    "trim",
+    "vehicle_type",
+    "body_class",
+    "doors",
+    "drive_type",
+    "fuel_type_primary",
+    "engine_cylinders",
+    "engine_displacement_l",
+    "transmission",
+    "plant_country",
+    "manufacturer",
+    "all_attributes"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "vin": "1HGBH41JXMN109186"
+  },
+  {
+    "vin": "5TDJKRFH4LS123456"
+  }
+]
  3. 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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds behavioral context by listing the specific fields returned (make, model, year, body style, engine type, drivetrain, plant). It does not mention error handling or edge cases, but the annotations cover the main behavioral concerns.

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 concise sentences: the first states the action and result, the second gives an example and cross-references alternative tools. Every element earns its place with no redundant filler or restating of the tool name.

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?

The tool is simple with one parameter, a full output schema exists, and the description provides a clear outcome (vehicle details) with specific fields and an example. It also situates the tool among siblings by naming where to go for recalls/ratings, making the context complete.

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 input schema already describes the vin parameter as a 17-character VIN with an example, achieving 100% schema description coverage. The description repeats the example but does not add significant new semantic meaning beyond what the schema provides. Baseline 3 is appropriate given the high schema coverage.

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 function with a specific verb and resource: 'Decode a VIN to get vehicle details.' It distinguishes itself from siblings by explicitly stating the return fields and directing users to get_recalls / get_safety_ratings for related but different data.

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?

The description provides explicit guidance on when to use this tool vs alternatives: 'For recalls/ratings use get_recalls / get_safety_ratings.' This exclusion clearly defines the scope of decode_vin, and the example input further clarifies the expected use case.

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

A3.8/5.0
Disambiguation3/5

The descriptions are extraordinarily detailed and do a lot of work to differentiate tools, but there is real functional overlap: three ask_pipeworx variants, six Polymarket/bet tools (bet_research, polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) that all target identifying betting/value opportunities, and overlapping ai_visibility_check vs scan_competitor_ai_presence. A capable agent could navigate it, but misselection risk is high.

Naming Consistency3/5

Mostly snake_case and readable, but the verb/noun placement is inconsistent: verb-first (get_makes, list_subscriptions, resolve_entity, decode_vin) mixes with noun-first (entity_profile, bet_research, pipeworx_trending) and branded prefixes (ask_pipeworx, pipeworx_feedback, polymarket_*). No chaotic camelCase mixing, but no single predictable pattern either.

Tool Count2/5

37 tools is well beyond the heavy threshold, and the server named 'Nhtsa' carries only ~6 vehicle-specific tools while the rest is a general-purpose research platform spanning prediction markets, memory, npm packages, AI-marketing audits, and subscriptions. The scope is overloaded and the name badly misrepresents the content, making the surface feel sprawling rather than focused.

Completeness4/5

For the NHTSA vehicle domain it covers the lookup surface well (makes, models, recalls, complaints, safety ratings, VIN decode), and the broader research platform is genuinely deep with grounding, grounding-with-evidence, discovery, subscription, and memory support. Minor gaps exist (no direct vehicle-make year filtering beyond three fields, USPTO patent APIs are soft-failing), but no dead ends for core workflows.

Resources