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pulsenetwork

by com.theaslangroupllc.pulse
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autopulse

AutoPulse: Automotive intelligence API — 10 endpoints powered by NHTSA, EPA, and live market data. Safety recalls, reliability analysis, DIY repair guides, vehicle comparison, market value, EV break-even, dealer

Coverage: Global

Endpoints: • vin-recall ($0.02): US safety recalls for a VIN — decode + NHTSA campaigns, deterministic • recall ($0.05): NHTSA safety recall lookup • problems ($0.10): Known problems and reliability analysis • repair ($0.10): DIY repair guide • compare ($0.10): Vehicle comparison • value ($0.08): Market value estimate • ev-breakeven ($0.10): EV break-even analysis vs. gas vehicle • negotiate ($0.10): Car buying negotiation guide • inspect ($0.08): Pre-purchase inspection checklist • parts ($0.08): Parts pricing and sourcing • tco ($0.15): Total cost of ownership (5-year) • financing ($0.12): Car financing / auto loan APR intelligence

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aprNoAPR percent — combine with amount + term_months for a deterministic monthly payment
jobNoRepair job (e.g. brake-pads, oil-change, cabin-air-filter)
vinNo17-character VIN
langNolang
makeNomake
partNoPart name (e.g. brake-pads, alternator, water-pump)
trimNotrim
yearNoyear
modelNomodel
stateNoUS state for state-level incentives and electricity rates
actionYesWhich endpoint to call. Options: vin-recall | recall | problems | repair | compare | value | ev-breakeven | negotiate | inspect | parts | tco | financing
amountNoLoan principal — combine with apr + term_months for a deterministic monthly payment
countryNoCountry context for APR landscape and financing structures
mileageNomileage
vehicleNoVehicle descriptor (e.g. 2020-toyota-camry)
ev_modelNoEV model name (e.g. Tesla Model 3, Chevrolet Bolt, Ford F-150 Lightning)
vehiclesNoComma-separated vehicles (e.g. Toyota RAV4,Honda CR-V,Mazda CX-5)
conditionNocondition
gas_priceNoLocal gas price in $/gallon
credit_tierNocredit_tier
gas_vehicleNoGas vehicle for comparison (e.g. Toyota Camry, Honda CR-V)
term_monthsNoLoan term in months, e.g. 36/48/60/72/84
annual_milesNoAnnual mileage (default: 12,000)
down_paymentNodown_payment
purchase_priceNoPurchase price in USD
electricity_rateNoLocal electricity rate in $/kWh

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / action / description
      Previous value: -"Which endpoint to call. Options: recall | problems | repair | compare | value | ev-breakeven | negotiate | inspect | parts | tco | financing"New value: +"Which endpoint to call. Options: vin-recall | recall | problems | repair | compare | value | ev-breakeven | negotiate | inspect | parts | tco | financing"
    • changedInput schema / properties / action / enum
      Previous value: -[
-  "recall",
-  "problems",
-  "repair",
-  "compare",
-  "value",
-  "ev-breakeven",
-  "negotiate",
-  "inspect",
-  "parts",
-  "tco",
-  "financing"
-]New value: +[
+  "vin-recall",
+  "recall",
+  "problems",
+  "repair",
+  "compare",
+  "value",
+  "ev-breakeven",
+  "negotiate",
+  "inspect",
+  "parts",
+  "tco",
+  "financing"
+]
  2. First observed

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries full responsibility. It adds useful behavioral details such as per-request pricing, determinism for specific endpoints (vin-recall, financing), and data sources (NHTSA, EPA, market data). However, it lacks disclosure of error handling, rate limits, or output formats, and there is an internal contradiction: claims 'Coverage: Global' but several endpoints are US-specific (vin-recall, state incentives).

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 well-structured with a front-loaded summary and a bullet list of endpoints with pricing. Each line is compact and informative. However, it contains an inconsistency (10 vs 12 endpoints) and includes pricing details that, while useful, add length. Overall it is efficient and readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (26 parameters, 12 actions, no output schema), the description is insufficient. It lists endpoints but does not explain required parameter combinations for each action (e.g., what inputs are needed for 'recall' vs 'compare'). It also does not describe return values or error behavior. This leaves significant gaps for an agent trying to invoke the tool correctly.

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 schema covers 100% of parameters, so baseline is 3. The description adds indirect parameter context by associating endpoints with likely inputs (e.g., vin-recall implies the 'vin' parameter, compare implies 'vehicles'), but it does not provide a systematic mapping of parameters to actions. It adds no syntax or format details beyond what the schema already offers.

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 it as an automotive intelligence API with specific endpoints (recalls, repair, comparison, value, etc.), powered by NHTSA, EPA, and market data. This distinguishes it from sibling 'pulse' tools by domain. The only issue is an inconsistency (says '10 endpoints' but lists 12), which does not affect the overall clarity of purpose.

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 provides clear usage context by enumerating 12 distinct endpoints with one-line summaries, making it obvious when to use this tool (e.g., VIN recall lookup, vehicle comparison, EV break-even analysis). It does not explicitly discuss alternatives or exclusions, but the domain-specific list implies appropriate use cases.

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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