Kalshi Eye
PYPI · KALSHIEYE-MCP
Search Kalshi, Polymarket and Limitless markets, order book history, coverage and data files.
Available components
Last scanned (1 day ago)
Scores higher than 82% of PyPI servers
Category Finance and Payments
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 Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs setuptools.build_meta at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 31 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 KalshiEye/kalshieye-mcp). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 6 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability60
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1468 tokens (~133/item across 11 items; 11 tools + 0 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 Management37
- Stability observed for 11 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage71
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
- 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 12 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.
- 3 Nov 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.
- 1 Nov 26 +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.
- 30 Oct 26 +16
- Malware scan: unverified → pass ▲ security
- 28 Oct 26 0
- Schema quality: pass → fail ▼ functional
- Package version: 0.2.1 → 0.2.2 functional
- 26 Oct 26 +1
- Stability: unverified → 0.07 ▲ functional
- Package version: 0.2.0 → 0.2.1 functional
- 24 Oct 26 62
First indexed and scored.
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 4 Nov 2026 · Analysed pypi/kalshieye-mcp@0.2.2
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 | KalshiEye/kalshieye-mcp |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/KalshiEye/kalshieye-mcp/.github/workflows/publish.yml@refs/tags/v0.2.2 |
| Rekor log index | 3531436512 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:b0d7dd1f622aaf45bd25fe1ed7d8e2e6e486176aa6d9b782616deb11a0d7ade5 |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Background: Why install scripts are a supply-chain risk →
Dependencies 31 packages
| Packages resolved | 31 |
|---|---|
| No linked repository | 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 →
dataset_series ~92
Which series have files in one dataset (for example book_l2 or trades), with days, rows and size. limit is 1 to 500 series a page and total counts them all. When next_offset is set, call again with it as offset for the next page.
| Name | Type | Req | Description |
|---|---|---|---|
| dataset_id | string | yes | – |
| limit | integer | – | – |
| offset | integer | – | – |
| query | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
download_links ~46
Signed download URLs for up to 100 file keys from list_files. The first download of a file in a month counts toward the plan's monthly allowance.
| Name | Type | Req | Description |
|---|---|---|---|
| keys | array | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_market ~80
One market: terms, status, result, and how much of it the archive recorded. ticker is a Kalshi market ticker (KXBTC15M-26OCT121545-45) or a Polymarket or Limitless market id as search returns it (pm:0x..., lm:...).
| Name | Type | Req | Description |
|---|---|---|---|
| ticker | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_series ~73
One series (for example KXBTC15M, KXFEDDECISION, or a pm:/lm: id from search): what it is, how often it runs, how many markets the catalog lists, and how many days of data files the archive has for it.
| Name | Type | Req | Description |
|---|---|---|---|
| series_id | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
list_datasets ~56
Every dataset in the archive (order books, top of book, minute bars, trades, settlements, Polymarket, Limitless, Chainlink...): what it holds, the plan it needs, its dates, and its columns.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
list_events ~165
Every event on every venue whose words (id, title, subtitle, series id and name, category) hold each word of query, newest close first, a page at a time: what search's more_events lists. status is live, closed or settled. limit is 1 to 200 a page. total is exact up to 10,000, and past that it is 10,000 with total_exact false (say "10,000+"). Pass next_cursor back as cursor until it is null to get every event; get_series or list_markets(series=...) open one.
| Name | Type | Req | Description |
|---|---|---|---|
| category | – | – | – |
| cursor | – | – | – |
| limit | integer | – | – |
| query | string | yes | – |
| status | – | – | – |
| venue | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
list_files ~84
Daily Parquet files for a dataset and date range (YYYY-MM-DD, UTC), optionally one series. When next_cursor is set, call again with it for the next page.
| Name | Type | Req | Description |
|---|---|---|---|
| cursor | – | – | – |
| dataset | string | yes | – |
| end_date | string | yes | – |
| limit | integer | – | – |
| series | – | – | – |
| start_date | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
list_markets ~245
Markets a page at a time, newest first, with status, result and times. With series, one series' markets (q then narrows them by text in the ticker, title or event). With q and no series, markets on every venue holding each of its whole words in the ticker, series and its name, title, event or category (venue narrows it to one venue). With neither, every Kalshi market by close. status is a market status such as active, closed or finalized. limit is 1 to 200 a page. total counts every match (null only on the plain all-markets list); with q it is exact up to 10,000, and past that it is 10,000 with total_exact false (say "10,000+"). To get every market: with q, pass next_cursor back as cursor until it is null; otherwise pass next_offset back as offset until it is null.
| Name | Type | Req | Description |
|---|---|---|---|
| cursor | – | – | – |
| limit | integer | – | – |
| offset | integer | – | – |
| q | – | – | – |
| series | – | – | – |
| status | – | – | – |
| venue | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
market_price_history ~146
The recorded YES best bid and ask path for one market: up to about 240 points over the market's busiest stretch (its whole life when that is short). path_source says which: "life" or "busiest", or "window" when the 1-minute bars give no path and the points come from the recorded order book in the preview's window. The window is the span the points cover. For every order book change, use the daily Parquet files (list_datasets, list_files). Previews are built for Kalshi markets; Polymarket and Limitless markets may return no points.
| Name | Type | Req | Description |
|---|---|---|---|
| ticker | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
recording_status ~33
Whether the recorder is running now, the last published day, and the recorded share of recent days, overall and by venue.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
search ~297
Search Kalshi, Polymarket and Limitless series, events and markets by keyword. Examples: "bitcoin 15 minute", "fed rate", "NFL", "New York temperature". category is an archive category such as Crypto, Economics, Sports or Politics. status "live" keeps markets still trading. series is one page: limit is 1 to 50 series, series_total counts every matching series, and when has_more is true, call again with offset + limit for the next page. Series lead: a query naming a series finds it first. events and markets are the 10 best whose own words (title, ticker, event, category, series name) hold every query word: still trading and closing soonest first, then the latest closed. They are not all of them: list_events and list_markets with q (and venue when given) list every match, as more_events / more_markets say. events_found and markets_found count those lists exactly up to 10,000; past that the count is 10,000 and events_found_exact / markets_found_exact is false (say "10,000+"). All are null when the query has no word to match (only dates or single letters).
| Name | Type | Req | Description |
|---|---|---|---|
| category | – | – | – |
| limit | integer | – | – |
| offset | integer | – | – |
| query | string | yes | – |
| status | – | – | – |
| venue | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the Kalshi Eye MCP server?
Kalshi Eye runs locally as a PyPI package, launched with uvx kalshieye-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 · kalshieye-mcp
claude mcp add kalshieye-kalshieye-mcp -- uvx kalshieye-mcp
What is the Kalshi Eye MCP server?
Kalshi Eye is an MCP server listed in the public MCP registry as io.github.KalshiEye/kalshieye-mcp. Search Kalshi, Polymarket and Limitless markets, order book history, coverage and data files. This page covers its PyPI package (kalshieye-mcp).
Is the Kalshi Eye MCP server safe to use?
Kalshi Eye scores 81 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 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 Kalshi Eye MCP server expose?
Kalshi Eye exposes 11 tools: search, get_series, list_markets, list_events, get_market, and 6 more. Their descriptions and schemas cost roughly 1,317 tokens of context every time the server is loaded.
Is the Kalshi Eye MCP server still maintained?
Kalshi Eye is still listed as active in the MCP registry. We last reached this channel on 4 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 Kalshi Eye MCP server under?
Kalshi Eye declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.