io.github.pouriaetab/llmledger
PYPI · LLMLEDGER-MCP
Which AI model, and what it costs: LiteLLM prices, Arena ratings and price history.
Available components
Last scanned (2 hours ago)
Scores higher than 67% of PyPI servers
Category AI and Machine Learning
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 Security40
- Malware scan not yet available for this package.Unverified
- CVE check failed: a known medium-severity CVE affects fastmcp 4.1.0, a direct dependency. A fixed version is available. View diagnostics → Fail
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 15 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to pouriaetab/llmledger). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 14 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability76
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 2558 tokens (~213/item across 12 items; 11 tools + 1 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management60
- Stability observed for 18 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
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
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 11 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 13 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
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.
- 5 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 3 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 50 to 53. That category is still filling its 30-day observation window: 15 days of observed history at the previous scan, 16 at this one. The score rises as the window fills, whether or not the server changes.
- 1 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.
- 31 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 30 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.
- 29 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 28 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.
- 26 Oct 26 +16
- Malware scan: unverified → pass ▲ security
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 5 Nov 2026 · Analysed pypi/llmledger-mcp@0.2.0
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | pouriaetab/llmledger |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/pouriaetab/llmledger/.github/workflows/mcp.yml@refs/tags/mcp-v0.2.0 |
| Rekor log index | 3356685301 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:019da1f1d77e9fa12a73d80d1340c9afda5241680ad13522470f38aaecefb50f |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Vulnerabilities 2 findings
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-7xgj-8392-5q4r | medium | CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:N | yes | |
| GHSA-vqqp-x5qm-5qp4 | medium | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:N/A:N | yes |
Background: What a vulnerability scan can and cannot prove →
Dependencies 15 packages
| Packages resolved | 15 |
|---|---|
| Stale | 1 |
| 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 →
compare_models Compare Models ~225
Compare 2 to 6 named models for a kind of work: rating, prices, cost per task (or how fast each uses a Claude or ChatGPT plan), the pick, and for each model the share of answer tokens at which it would beat the pick. Use when the user already has candidate models in mind. Not for finding candidates (find_models), for reading a prompt to guess the work (suggest_for_project), or for a monthly budget (estimate_cost).
| Name | Type | Req | Description |
|---|---|---|---|
| answer_tokens | – | – | Answer tokens per model when they differ, by the names given |
| input_tokens | integer | – | Prompt tokens per task |
| models | array | yes | 2 to 6 model names, for example claude-opus-5.5, gpt-6.1-sol |
| output_tokens | integer | – | Answer tokens per task, including hidden thinking |
| pay | string | – | api: dollars; claude or chatgpt: how fast each uses that plan |
| task | string | – | The kind of work, as Arena rates it |
Structured output declared, but exposes no named fields.
No examples provided.
estimate_cost Estimate Cost ~186
Price a workload on 1 to 10 models at each one's cheapest listing: dollars per call, per day and per 30 days, and with cache tokens the saving the cache makes. Use when the question is a budget or a bill: token counts and calls a day are known. Not for deciding which model is better (compare_models) or for pricing past usage from an export (price_my_usage).
| Name | Type | Req | Description |
|---|---|---|---|
| cache_write_tokens | integer | – | How many of the prompt tokens are written to the cache |
| cached_tokens | integer | – | How many of the prompt tokens are read from the cache |
| calls_per_day | number | – | Calls per day |
| input_tokens | integer | yes | Prompt tokens per call |
| models | array | yes | 1 to 10 model names |
| output_tokens | integer | yes | Answer tokens per call, including hidden thinking |
Structured output declared, but exposes no named fields.
No examples provided.
estimate_time Estimate Time ~194
Estimate how long one call and a whole job take, as a usual time and a slow day, from the user's own timed runs of that model called that way (kept by log_run). Use when the question is time, not money, and some runs have been logged; it says so when none have. Not for cost (estimate_cost) or for a job you have a promptfoo sample of (forecast_job gives time and cost together).
| Name | Type | Req | Description |
|---|---|---|---|
| calls | integer | – | How many calls the job makes |
| concurrency | integer | – | Calls running at once |
| input_tokens | integer | yes | Prompt tokens per call |
| model | string | yes | A model name, as the catalogue or Ollama names it |
| output_tokens | integer | yes | Answer tokens per call, including hidden thinking |
| thinking | boolean | – | Thinking turned on |
| way | string | – | How the model is called |
Structured output declared, but exposes no named fields.
No examples provided.
find_models Find Models ~227
List the highest rated models for a kind of work within price, prompt size, ability and maker limits, each at its cheapest listing with its rating; or, with `name`, look one model up and list every host that offers it. Use when browsing or filtering the catalogue, or to find a model's exact name. Not for choosing between named models (compare_models), for a prompt you have in hand (suggest_for_project), or for a budget (estimate_cost).
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | How many models to list (up to 200 for the whole catalogue) |
| maker | – | – | Only this maker, for example anthropic or openai |
| max_output_price | – | – | Highest answer price, dollars per 1M tokens |
| min_context | – | – | Smallest prompt size the model must take, in tokens |
| name | – | – | Look up one model by name instead of browsing: all its listings |
| needs | array | – | What the model must be able to do |
| task | string | – | The kind of work, as Arena rates it |
Structured output declared, but exposes no named fields.
No examples provided.
forecast_job Forecast Job ~250
Estimate a whole job's time, cost and accuracy from a promptfoo run of a sample of it: for each model in the sample, the time as a usual time and a slow day, the cost at its cheapest listing next to the listing the sample used, the 90-day chance of a price change, how often it was right (95% range) and the break-even against the pick. Use when a promptfoo results file of a sample exists and the whole job's size is known. Not for a job with no sample (estimate_cost for money, estimate_time for time).
| Name | Type | Req | Description |
|---|---|---|---|
| concurrency | – | – | Calls running at once in the whole job (default: as the sample ran) |
| items | integer | yes | How many tests the whole job has |
| promptfoo_results | string | yes | Path of a promptfoo results file from a sample of the job (promptfoo eval --filter-sample 20 -o sample.json) |
| thinking | boolean | – | Thinking turned on |
| way | – | – | How the models are called (default: read from each provider) |
| wrong_answer_cost | – | – | Dollars a wrong answer costs you to catch and fix; the pick then counts it |
Structured output declared, but exposes no named fields.
No examples provided.
local_or_cloud Local Or Cloud ~329
Compare running an open model on a computer you buy with paying a cloud model per call: the cost a month each way, the month the computer pays for itself, and whether it keeps up with the load. Use when the question is whether to buy hardware for a local model. Needs timed runs of the local model (log_run) and the computer's price; electricity comes from EIA or the user's own rate. Not for cloud-only comparisons (compare_models or estimate_cost).
| Name | Type | Req | Description |
|---|---|---|---|
| cached_tokens | integer | – | Prompt tokens read from the cloud model's cache |
| calls_per_month | number | yes | Calls a month |
| cloud_models | array | yes | 1 to 5 cloud models to compare with |
| electricity_price | – | – | Your electricity price, dollars per kWh (default: EIA's average for homes) |
| hardware_price | number | yes | What the computer costs you, in dollars |
| input_tokens | integer | yes | Prompt tokens per call |
| local_model | string | yes | The open model you would run at home, as Ollama names it, for example llama3.1:8b |
| local_way | string | – | How the local model is called |
| months | integer | – | How many months to compare over |
| output_tokens | integer | yes | Answer tokens per call, including hidden thinking |
| state | – | – | A US state, for EIA's average there, for example Texas |
| thinking | boolean | – | Thinking turned on for the local model |
| watts | – | – | Power the computer draws while it answers, in watts |
Structured output declared, but exposes no named fields.
No examples provided.
log_run Log Run ~215
Record a timed run (model, tokens, seconds, how it was called), or every result of a promptfoo results file, in a file on this computer so estimate_time can learn from it. Use right after a call or a promptfoo run whose time is known. Not a query: it stores and returns what it stored. Nothing is sent anywhere.
| Name | Type | Req | Description |
|---|---|---|---|
| input_tokens | – | – | Prompt tokens of the call |
| model | – | – | The model that ran |
| output_tokens | – | – | Answer tokens of the call, hidden thinking included |
| promptfoo_results | – | – | Path of a promptfoo results file (promptfoo eval -o results.json): every result becomes a run |
| seconds | – | – | How long the call took |
| task | – | – | The kind of work, if known |
| thinking | boolean | – | Thinking was on (a promptfoo result that counts thinking tokens is marked on by itself) |
| way | – | – | How the model was called (default api; for a promptfoo file, read from each provider) |
Structured output declared, but exposes no named fields.
No examples provided.
not_yet_priced Not Yet Priced ~96
List the models Arena has rated that LiteLLM's catalogue does not price yet, with how soon the catalogue usually prices a maker's models after Arena first shows them. Use when a brand-new model is missing from the other tools' answers, or to ask what is coming. Not for models that already have a price (find_models).
| Name | Type | Req | Description |
|---|---|---|---|
| maker | – | – | Only this maker, for example meta or ibm |
Structured output declared, but exposes no named fields.
No examples provided.
price_history Price History ~177
Show one model's price history: every price and retirement date change since 2023 on every host, each with the LiteLLM change behind it, the chance of a price change, retirement or removal in the next 90 days and year, and with `date`, the prices on that day. Use when the question is about one model over time, a past price, or how likely a price is to move. Not for today's cheapest price of several models (find_models or estimate_cost).
| Name | Type | Req | Description |
|---|---|---|---|
| date | – | – | A day as YYYY-MM-DD: each listing's prices that day and the change that set them |
| model | string | yes | A model name, for example claude-opus-5.5, or one listing such as bedrock/anthropic.claude-opus-5-5 |
Structured output declared, but exposes no named fields.
No examples provided.
price_my_usage Price My Usage ~271
Price the user's own past coding agent usage, from a ccusage or codeburn export, on every rated model: each session at the price of its day, the models that keep the rating and cost less, a 95% range for the cost per session, and whether two models differ for real or the data cannot tell. Use when a usage export (or a list of token mixes) exists and the question is what the same work would have cost elsewhere. Not for a future workload (estimate_cost) or for a prompt (suggest_for_project). Reads token counts, names and dates only; nothing leaves the computer.
| Name | Type | Req | Description |
|---|---|---|---|
| compare | – | – | Two models to test against each other on the same sessions |
| export | – | – | Path of a token export on this computer: `ccusage daily --json > f.json`, `ccusage session --json > f.json`, or `codeburn export -f json -o f.json` |
| limit | integer | – | How many models to list in every_model (the cheapest first) |
| mix | – | – | Instead of a file: token mixes as {model, date, input, output, cache_read, cache_write, session?} |
| task | – | – | The kind of work the sessions were (default: codeburn's categories, else coding) |
Structured output declared, but exposes no named fields.
No examples provided.
suggest_for_project Suggest For Project ~155
Suggest a model and effort level for a prompt or a described project: it guesses the kind of work and names the best value (the cheapest model the votes cannot tell apart from the top), the top rated and the cheapest rated, each with its cost per task. Use when the user asks "which model should I use for this" and the work itself is in hand. Not for named candidates (compare_models) or for browsing by limits (find_models).
| Name | Type | Req | Description |
|---|---|---|---|
| answer | string | – | Typical answer length |
| pay | string | – | api: dollars; claude or chatgpt: compare within that plan |
| prompt | string | yes | The project's starting prompt, or a description of the work |
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the io.github.pouriaetab/llmledger MCP server?
io.github.pouriaetab/llmledger runs locally as a PyPI package, launched with uvx llmledger-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · llmledger-mcp
claude mcp add pouriaetab-llmledger -- uvx llmledger-mcp
What is the io.github.pouriaetab/llmledger MCP server?
io.github.pouriaetab/llmledger is an MCP server listed in the public MCP registry as io.github.pouriaetab/llmledger. Which AI model, and what it costs: LiteLLM prices, Arena ratings and price history. This page covers its PyPI package (llmledger-mcp).
Is the io.github.pouriaetab/llmledger MCP server safe to use?
io.github.pouriaetab/llmledger scores 71 out of 100 on VerifyMCP. We recorded 2 known advisories against it as of 5 November 2026. Its build provenance is signed and verified. 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.pouriaetab/llmledger MCP server expose?
io.github.pouriaetab/llmledger exposes 11 tools: find_models, compare_models, estimate_cost, price_history, suggest_for_project, and 6 more. Their descriptions and schemas cost roughly 2,325 tokens of context every time the server is loaded.
Is the io.github.pouriaetab/llmledger MCP server still maintained?
io.github.pouriaetab/llmledger is still listed as active in the MCP registry. We last reached this channel on 5 November 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.pouriaetab/llmledger MCP server under?
io.github.pouriaetab/llmledger declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.