# io.github.jeff-atriumn/tokencost-dev (npm · tokencost-dev)

LLM pricing oracle — model lookup, cost estimation, and comparison via LiteLLM

- Trust score: 80/100 (high trust)
- Change this week: +34
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `tokencost-dev`: 80/100 (this document), [markdown](https://verifymcp.io/servers/jeff-atriumn-tokencost-dev/tokencost-dev.md), [page](https://verifymcp.io/servers/jeff-atriumn-tokencost-dev/tokencost-dev)

## Channel facts

- Registry: `npm`
- Package: `tokencost-dev`
- Version: `0.1.3`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 87/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 97/100
  - Source repository is publicly reachable at the declared URL.
  - Cryptographically verified build provenance (signed, bound to atriumn/tokencost-dev).
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 157 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 77/100
  - AI-judged instruction clarity (excellent).
  - Tool/resource definitions use about 322 tokens (~80/item across 4 items; 4 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add jeff-atriumn-tokencost-dev -- npx -y tokencost-dev
```

### Codex

```bash
codex mcp add jeff-atriumn-tokencost-dev -- npx -y tokencost-dev
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "jeff-atriumn-tokencost-dev": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "tokencost-dev"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add jeff-atriumn-tokencost-dev --command npx --arg -y --arg tokencost-dev
```

### Hermes

```yaml
mcp_servers:
  jeff-atriumn-tokencost-dev:
    command: "npx"
    args: ["-y", "tokencost-dev"]
```

### Other

```json
{
  "mcpServers": {
    "jeff-atriumn-tokencost-dev": {
      "command": "npx",
      "args": [
        "-y",
        "tokencost-dev"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-02 (score 79, +15)

- [security regression] Known CVEs: partial → unverified
- [security improvement] Malware scan: unverified → pass
- [security] Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet.
- [functional regression] Security disclosure: fail → unverified
- [functional regression] Capabilities: pass → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional regression] Dependency health: partial → unverified
- [functional improvement] Stability: unverified → 0.23

### 2026-08-01 (score 64, +36)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-31 (score 28, −18)

- [security regression] Malware scan: pass → unverified

### 2026-07-30 (score 46, +22)

- [functional improvement] Tool coverage: unverified → 100

### 2026-07-28 (score 24, −22)

- [functional regression] Security disclosure: unverified → fail
- [functional regression] Tool coverage: 100 → unverified
- [functional] First check of Schema quality: unverified

### 2026-07-27 (score 46)

First indexed and scored.

## MCP tools (4)

### `get_model_details` (~79 tokens)

Look up pricing, context window, and capabilities for an LLM model. Uses fuzzy matching so you don't need the exact model key.

Input parameters:

- `model_name` (string, required): Model name to look up (e.g. 'claude-sonnet-4-5', 'gpt-4o', 'gemini-2.0-flash')

### `calculate_estimate` (~112 tokens)

Estimate the cost for a given number of input and output tokens on a specific model. Supports optional cached_tokens for prompt caching discounts.

Input parameters:

- `cached_tokens` (number): Number of input tokens served from cache (prompt caching). Must be <= input_tokens. These tokens are billed at the cached rate instead of the standard input rate.
- `input_tokens` (number, required): Number of input tokens
- `model_name` (string, required): Model name (fuzzy matched)
- `output_tokens` (number, required): Number of output tokens

### `compare_models` (~98 tokens)

Filter and compare models by provider, minimum context window, or mode. Returns top 5 most cost-effective matches.

Input parameters:

- `min_context` (number): Minimum context window size in tokens
- `mode` (string): Filter by mode (e.g. 'chat', 'embedding', 'completion', 'image_generation')
- `provider` (string): Filter by provider (e.g. 'anthropic', 'openai', 'google', 'amazon')

### `refresh_prices` (~33 tokens)

Force a re-fetch of pricing data from the LiteLLM registry. Use this if you suspect the cached data is stale.

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/jeff-atriumn-tokencost-dev/tokencost-dev#diagnostics

## Score history

- 2026-08-03: 80
- 2026-08-02: 79
- 2026-08-01: 64
- 2026-07-31: 28
- 2026-07-30: 46
- 2026-07-28: 24
- 2026-07-27: 46

## Links

- npm package: https://www.npmjs.com/package/tokencost-dev
- Socket report: https://socket.dev/npm/package/tokencost-dev
- Repository: https://github.com/atriumn/tokencost-dev
- Changelog RSS feed: https://verifymcp.io/servers/jeff-atriumn-tokencost-dev/tokencost-dev/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/jeff-atriumn-tokencost-dev/tokencost-dev/changelog.json
- HTML version of this page: https://verifymcp.io/servers/jeff-atriumn-tokencost-dev/tokencost-dev