io.github.vpatser1/ai-cost-analyzer
NPM · AI-COST-ANALYZER · SCANNED SEP 24
Analyze LLM API costs: token waste detection, caching savings estimates, model comparison
Available components
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. How we score → Why this is hard to score →
Supply Chain Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 31 of 96 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency19
- Repository check failed: no source repository is declared. See how to fix → View diagnostics → Fail
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 50 days ago).Pass
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability70
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 375 tokens (~75/item across 5 items; 5 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the io.github.vpatser1/ai-cost-analyzer MCP server?
io.github.vpatser1/ai-cost-analyzer runs locally as an npm package, launched with npx -y ai-cost-analyzer. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · ai-cost-analyzer
claude mcp add vpatser1-ai-cost-analyzer -- npx -y ai-cost-analyzer
{
"mcpServers": {
"vpatser1-ai-cost-analyzer": {
"command": "npx",
"args": [
"-y",
"ai-cost-analyzer"
]
}
}
} {
"servers": {
"vpatser1-ai-cost-analyzer": {
"command": "npx",
"args": [
"-y",
"ai-cost-analyzer"
]
}
}
} codex mcp add vpatser1-ai-cost-analyzer -- npx -y ai-cost-analyzer
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"vpatser1-ai-cost-analyzer": {
"type": "local",
"command": [
"npx",
"-y",
"ai-cost-analyzer"
],
"enabled": true
}
}
} openclaw mcp add vpatser1-ai-cost-analyzer --command npx --arg -y --arg ai-cost-analyzer
mcp_servers:
vpatser1-ai-cost-analyzer:
command: "npx"
args: ["-y", "ai-cost-analyzer"] {
"McpServers": {
"vpatser1-ai-cost-analyzer": {
"Transport": "stdio",
"Command": "npx",
"Arguments": [
"-y",
"ai-cost-analyzer"
]
}
}
} assistant mcp add vpatser1-ai-cost-analyzer -t stdio -c npx -a -y ai-cost-analyzer
{
"mcpServers": {
"vpatser1-ai-cost-analyzer": {
"command": "npx",
"args": [
"-y",
"ai-cost-analyzer"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 24 Sept 26 +1
- Stability: 0.97 → pass security
- 22 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 20 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 18 Sept 26 −3
- Stability: pass → 0.80 functional
- 17 Sept 26 +1
- Stability: 0.97 → pass security
- 15 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 13 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 11 Sept 26 −3
- Stability: pass → 0.80 functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 24 Sept 2026 · Analysed npm/ai-cost-analyzer@1.1.4
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Dependencies 96 packages
| Packages resolved | 96 |
|---|---|
| Stale | 31 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
analyze_usage ~56
Analyze AI API usage data and return cost breakdowns by model, average cost per request, token waste estimation, and daily/weekly trends.
| Name | Type | Req | Description |
|---|---|---|---|
| usage_data | array | yes | Array of API usage records with model, tokens, and timestamps |
No output schema declared.
No examples provided.
compare_models ~93
Compare cost and quality tradeoffs across Claude, GPT-4o, GPT-4o-mini, and Gemini models for a given task. Returns cost per request, monthly projections, and recommendations.
| Name | Type | Req | Description |
|---|---|---|---|
| estimated_input_tokens | integer | – | Override estimated input tokens per request |
| estimated_output_tokens | integer | – | Override estimated output tokens per request |
| task_description | string | yes | Description of the task to compare models for |
No output schema declared.
No examples provided.
estimate_savings ~76
Estimate potential monthly savings from prompt caching, model routing, and context pruning based on current usage patterns.
| Name | Type | Req | Description |
|---|---|---|---|
| monthly_multiplier | number | – | If the usage_data is a sample, multiply costs by this factor to project monthly totals (default: 1) |
| usage_data | array | yes | Array of API usage records representing current usage patterns |
No output schema declared.
No examples provided.
get_pricing ~39
Return current pricing for all major LLM APIs (Claude, OpenAI, Gemini) with input, output, and cached token rates per million tokens.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
optimize_prompt ~111
Analyze a system prompt and tool definitions, return an optimized version with redundant content removed, token counts before/after, and cost reduction estimates.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | – | Model name for pricing calculation (default: claude-sonnet) |
| requests_per_month | integer | – | Estimated requests per month for cost projection (default: 10,000) |
| system_prompt | string | yes | The system prompt text to optimize |
| tool_definitions | array | yes | Array of tool definition strings (JSON or plain text) |
No output schema declared.
No examples provided.
What is the io.github.vpatser1/ai-cost-analyzer MCP server?
io.github.vpatser1/ai-cost-analyzer is an MCP server listed in the public MCP registry as io.github.vpatser1/ai-cost-analyzer. Analyze LLM API costs: token waste detection, caching savings estimates, model comparison. This page covers its npm package (ai-cost-analyzer).
Is the io.github.vpatser1/ai-cost-analyzer MCP server safe to use?
io.github.vpatser1/ai-cost-analyzer scores 78 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 24 September 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.vpatser1/ai-cost-analyzer MCP server expose?
io.github.vpatser1/ai-cost-analyzer exposes 5 tools: analyze_usage, estimate_savings, optimize_prompt, compare_models, get_pricing. Their descriptions and schemas cost roughly 375 tokens of context every time the server is loaded.
Is the io.github.vpatser1/ai-cost-analyzer MCP server still maintained?
io.github.vpatser1/ai-cost-analyzer is still listed as active in the MCP registry. We last reached this channel on 24 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.vpatser1/ai-cost-analyzer MCP server under?
io.github.vpatser1/ai-cost-analyzer declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.