MCP-Server, der die Storm-API für plattformübergreifende Prognosemarkt-Intelligenz für jeden Model Context Protocol-Client verfügbar macht.
Storm ist der autonome KI-Agent, der Eyewall Markets betreibt, einen plattformübergreifenden Dienst für Prognosemarkt-Intelligenz, der Polymarket, Kalshi, Manifold, Futuur, Betfair, ForecastEx und mehr umfasst. Dieses Paket ist eine schlanke stdio-MCP-Brücke, die es LLM-Clients – Claude Desktop, Claude Code, Cursor, Zed und jedem anderen MCP-fähigen Host – ermöglicht, Storms kanonische Ereignisse, plattformübergreifende Spreads, den Plattformkatalog und den Posteingang für benutzerbezogene Warnmeldungen als native Tool-Aufrufe zu lesen.
Es richtet sich an Analysten, Trader und Agent-Entwickler, die bereits ein Storm-Abonnement besitzen und möchten, dass ihre LLM-Arbeitsumgebung sieht, was Storm sieht.
Die Storm-API ist auf Edge-Tarif-Abonnenten (499 $/Monat) beschränkt. Generieren Sie Ihren
api_keyunter https://eyewallmarkets.com/account. Niedrigere Tarife haben keinen API-Zugriff und erhalten bei jedem Endpunkt, den dieser Server aufruft, einenHTTP 403-Fehler.
API-Schlüssel sind als stk_ gefolgt von 48 Hexadezimalzeichen (insgesamt 52 Zeichen) formatiert und an ein einzelnes Konto gebunden. Behandeln Sie diese wie jede andere Inhaber-Anmeldeinformation.
Die vollständige API-Referenz finden Sie unter https://eyewallmarkets.com/api/docs.
Related MCP server: pmxt-mcp
Bearbeiten Sie Ihre Claude Desktop MCP-Konfigurationsdatei und fügen Sie einen storm-Eintrag unter mcpServers hinzu:
{
"mcpServers": {
"storm": {
"command": "npx",
"args": ["-y", "@eyewallmarkets/storm-mcp"],
"env": {
"STORM_API_KEY": "stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}Der macOS-Pfad lautet:
~/Library/Application Support/Claude/claude_desktop_config.jsonLinux:
~/.config/Claude/claude_desktop_config.json· Windows:%APPDATA%\Claude\claude_desktop_config.json.
Starten Sie Claude Desktop neu. Die sieben storm_*-Tools sollten in der Tool-Liste innerhalb jeder neuen Konversation erscheinen.
claude mcp add storm npx -- -y @eyewallmarkets/storm-mcpExportieren Sie dann den API-Schlüssel in die Umgebung, in der Claude Code den Server startet (oder setzen Sie ihn in Ihrem Shell-Profil):
export STORM_API_KEY=stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxDer genaue CLI-Aufruf kann je nach Claude Code-Version variieren; siehe die offizielle Dokumentation unter https://docs.claude.com/en/docs/claude-code/mcp für die kanonische Form (einschließlich der Art und Weise, wie Umgebungsvariablen in der mcp add-Zeile selbst übergeben werden).
Führen Sie nach der Installation /mcp innerhalb von Claude Code aus, um zu bestätigen, dass der storm-Server verbunden ist und die sieben Tools registriert sind.
Cursor liest MCP-Serverdefinitionen aus ~/.cursor/mcp.json. Fügen Sie die gleiche Struktur hinzu, die für Claude Desktop verwendet wird:
{
"mcpServers": {
"storm": {
"command": "npx",
"args": ["-y", "@eyewallmarkets/storm-mcp"],
"env": {
"STORM_API_KEY": "stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}Laden Sie die MCP-Integration von Cursor neu (Einstellungen → MCP → Aktualisieren).
Zed konfiguriert MCP-Server unter assistant.context_servers in ~/.config/zed/settings.json:
{
"assistant": {
"context_servers": {
"storm": {
"command": "npx",
"args": ["-y", "@eyewallmarkets/storm-mcp"],
"env": {
"STORM_API_KEY": "stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}
}Starten Sie Zed neu oder laden Sie das Assistenten-Panel neu.
Die gesamte Konfiguration erfolgt über Umgebungsvariablen, die beim Serverstart gelesen werden.
Variable | Erforderlich | Standard | Beschreibung |
| ja | — | Edge-Tarif API-Schlüssel, Format |
| nein |
| Überschreiben der API-Basis-URL. Nützlich für Staging oder einen lokalen Entwicklungsserver. |
| nein |
| Timeout pro Anfrage in Millisekunden. |
| nein |
| Einer der Werte |
Alle sieben Tools sind schreibgeschützt und idempotent. Die Argumentstrukturen sind unten in einer JSON-Schema-ähnlichen Form dokumentiert; das Live-Schema ist das, was der MCP-Client tatsächlich sieht.
storm_list_eventsListet kanonische (plattformübergreifende) Ereignisse auf. Cursor-paginiert.
{
"limit": { "type": "integer", "default": 50, "max": 200 },
"cursor": { "type": "string", "optional": true },
"category": { "type": "string", "optional": true, "example": "politics" },
"status": { "type": "string", "optional": true, "enum": ["open", "closed", "resolved"] }
}Beispiel-Prompt: "Liste die nächsten 20 offenen politischen Ereignisse auf Storm auf."
storm_get_eventRuft ein einzelnes Ereignis anhand des Slugs ab, einschließlich aller verknüpften Ergebnisse und Preise pro Plattform.
{
"slug": { "type": "string", "required": true, "example": "us-pres-2028" }
}Beispiel-Prompt: "Rufe den vollständigen Storm-Datensatz für us-pres-2028 ab und sage mir, welches Ergebnis den größten plattformübergreifenden Spread aufweist."
storm_list_spreadsListet aktuelle plattformübergreifende Spreads auf, bei denen der Netto-Vorteil (nach Gebühren) den Mindestwert überschreitet. Sortiert nach Vorteil (DESC).
{
"min_edge_bps": { "type": "integer", "default": 100 },
"limit": { "type": "integer", "default": 50, "max": 200 },
"cursor": { "type": "string", "optional": true }
}Beispiel-Prompt: "Zeige mir die Top 10 Storm-Spreads mit mindestens 250 Basispunkten Vorteil im Moment."
storm_get_marketSucht einen einzelnen Plattform-/Marktdatensatz anhand von (venue_slug, external_id).
{
"venue": { "type": "string", "required": true, "example": "polymarket" },
"external_id": { "type": "string", "required": true }
}Beispiel-Prompt: "Suche den Polymarket-Markt 0xabc... auf Storm."
storm_list_venuesListet jede von Storm verfolgte Plattform auf, mit regulatorischem Status (CFTC-registriertes DCM, Offshore usw.), Gebührenstrukturen und Fähigkeits-Flags (Orderbuch, AMM, Parimutuel).
{}Beispiel-Prompt: "Welche von Storm verfolgten Plattformen sind CFTC-registrierte DCMs?"
storm_get_alerts_inboxFragt den API-Kanal-Posteingang für Warnmeldungen des Benutzers ab. Edge-Abonnenten können Warnmeldungen an den api-Zustellungskanal weiterleiten; dieses Tool leert die nicht bestätigte Warteschlange.
{
"since": { "type": "integer", "minimum": 0, "optional": true, "example": 4521 }
}Der Cursor ist die Ganzzahl id der letzten Warnmeldung, die Sie gesehen haben. Übergeben Sie next_since aus der vorherigen Antwort, um nur neuere Warnmeldungen abzurufen; lassen Sie es weg, um ab dem gespeicherten Bestätigungs-Cursor des Benutzers zu lesen. Beispiel-Prompt: "Frage meinen Storm-Posteingang ab und fasse alles zusammen, was ich noch nicht bestätigt habe."
storm_ack_alertsSetzt den persistenten Bestätigungs-Cursor vor, sodass zukünftige Posteingangsabfragen bereits behandelte Warnmeldungen überspringen. Der Cursor ist serverseitig und überlebt MCP-Sitzungen hinweg.
{
"up_to": { "type": "integer", "minimum": 0, "required": true, "example": 4530 }
}Beispiel-Prompt: "Bestätige alle Storm-Warnmeldungen bis jetzt."
Benutzer: Finde alle 2028-Wahl-Storm-Spreads mit einem Vorteil von über 300 Basispunkten und nenne mir die Top 3.
Assistent: (ruft
storm_list_spreadsmitmin_edge_bps: 300, limit: 50auf, filtert die Antwort nach dem Ereignis-Slug-Präfix2028_us_presidential_, ruft dannstorm_get_eventfür die Top 3 auf, um sie anzureichern)Gibt die drei größten aktuell offenen 2028-Wahl-Spreads zurück, das Plattformpaar auf jeder Seite und welche Plattform den günstigen Teil hält.
Benutzer: Frage meinen Storm-Posteingang ab und fasse nicht bestätigte Warnmeldungen zusammen, dann bestätige alles, was du zusammengefasst hast.
Assistent: (ruft
storm_get_alerts_inboxohnesinceauf, fasst die Elemente nach Kategorie zusammen, ruft dannstorm_ack_alertsmitup_toauf die größte geseheneidauf)Gibt eine kategorisierte Zusammenfassung nicht bestätigter Warnmeldungen zurück (Spread-Vorteils-Überschreitungen, Änderungen des Plattformstatus, Auflösungsereignisse) und bestätigt das Vorrücken des Cursors, sodass nachfolgende Abfragen nur neue Elemente zurückgeben.
Die Storm-API erzwingt serverseitig 10 Anfragen pro Sekunde pro API-Schlüssel. Wenn Sie dies überschreiten, gibt der Server HTTP 429 mit Retry-After: 1 zurück. Diese MCP-Brücke führt keine automatischen Wiederholungsversuche durch; sie zeigt den Fehler dem LLM als Klartextinhalt im Tool-Ergebnis an, damit das Modell entscheiden kann, ob es warten, wiederholen oder aufgeben soll.
Fehler werden dem LLM in dieser Form zurückgegeben (Textinhalt im Tool-Ergebnis, isError: true):
Storm API error (HTTP 429, rate_limited): too many requests Retry after 1000 ms.Storm API error (HTTP 403, edge_tier_required): edge_tier_requiredStorm API error (HTTP 401, invalid_credentials): invalid_credentialsNetzwerkfehler (DNS, TCP, TLS, Timeout) werden wie folgt angezeigt:
Storm API error (HTTP 0, transport): request timeoutDer vollständige HTTP-Status, der Storm-Fehlercode und die menschliche Nachricht sind immer enthalten, damit das LLM darauf reagieren kann.
git clone https://github.com/lsudduth/storm-mcp.git
cd storm-mcp
npm install
npm testVerweisen Sie auf einen lokalen Storm-Entwicklungsserver, indem Sie die API-Basis überschreiben:
STORM_API_BASE=http://localhost:8080/api/v1 \
STORM_API_KEY=stk_dev_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx \
node src/index.mjsDer Server spricht stdio-MCP, sodass Sie ihn direkt mit jedem MCP-Test-Harness oder durch manuelles Weiterleiten von JSON-RPC-Frames steuern können.
Tests verwenden den integrierten Test-Runner von Node; kein Jest, kein Vitest, kein Transpiler.
MIT — siehe LICENSE.
© 2026 XCH1TB, LLC dba Eyewall Markets.
Storm selbst — der autonome KI-Agent, der Eyewall Markets betreibt und die Daten produziert, die dieser Server bereitstellt — ist eine separate, interne Codebasis. Dieses Paket ist nur die clientseitige MCP-Brücke zur öffentlichen, schreibgeschützten API-Oberfläche von Storm.
Diese Software und die von ihr bereitgestellten Daten werden nur zu Informationszwecken bereitgestellt. Nichts, was von Storm oder diesem MCP-Server zurückgegeben wird, stellt eine Rechts-, Finanz-, Steuer- oder Anlageberatung dar. Die Teilnahme an Prognosemärkten unterliegt Ihren lokalen Gesetzen und den eigenen Zulassungsregeln der Plattform; insbesondere ist Polymarket in den Vereinigten Staaten gemäß CFTC-Anordnungen eingeschränkt und für die meisten US-Personen nicht verfügbar. Die Zulassung zur Plattform liegt in Ihrer Verantwortung, nicht bei Storm und nicht bei diesem Server. Storm aggregiert öffentlich beobachtbare Marktstatus und platziert keine Trades in Ihrem Namen.
Advance the persistent ack cursor to the given sequence id, removing items at or below it from the api-channel inbox. Call this after the LLM / agent has processed items returned by storm_get_alerts_inbox; otherwise the same items will keep being returned. Sourced from Eyewall Markets / Storm. The cursor is server-side and survives across MCP sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| up_to | Yes | Ack all alerts with sequence id <= up_to. Use the highest id seen in storm_get_alerts_inbox. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the cursor is persistent, server-side, survives sessions, and that items are removed. This is sufficient for a mutation tool of this complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences cover action, usage, and persistence. Every sentence adds necessary information with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple acknowledgment tool with one parameter and no output schema, the description covers behavior, usage context, and parameter guidance. Minor gap: does not mention any potential side effects, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and schema already describes the parameter. The description adds value by advising to use the highest id seen in storm_get_alerts_inbox, which is practical guidance beyond the raw schema definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('advance the persistent ack cursor' and 'removing items') and the resource ('api-channel inbox'). It distinguishes itself from the sibling tool storm_get_alerts_inbox by focusing on acknowledgment and removal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs to call this after processing items from storm_get_alerts_inbox to avoid duplicates. It provides clear context on when to use, though it does not elaborate on when not to use or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Poll the subscriber's api-channel notification inbox for cross-venue price-difference and event notifications that haven't been ack'd yet. Each item is a descriptive notification — it names the canonical event, the two venues, the prices each venue was publishing at the observation timestamp, and the rule that matched. Sourced from Eyewall Markets / Storm. Pass the next_since returned by the previous call as since to get only newer items. After processing, call storm_ack_alerts to advance the persistent cursor.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Only return alerts with sequence id strictly greater than this. Defaults to 0 (full inbox). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of explaining behavior. It states that the tool returns unacknowledged notifications, explains cursor-based pagination, and implies a read-only operation. It does not mention rate limits or other details, but the core behavioral traits are well covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at three sentences, each serving a distinct purpose: purpose explanation, content description, and usage pattern with next steps. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations) and the complexity of the polling pattern, the description is fully complete. It explains the tool, pagination, and the required follow-up action, leaving no critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'since' is fully described in the schema (100% coverage), and the description adds significant value by explaining its role in pagination and instructing how to use the 'next_since' value from previous calls. This goes beyond the schema's basic constraint.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the tool's purpose: to poll the subscriber's api-channel notification inbox for unacknowledged cross-venue price-difference and event notifications. It names the verb 'poll', the resource 'inbox', and details the content of each notification, distinguishing it from the related 'storm_ack_alerts' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on pagination (using 'next_since' from previous call as 'since') and directs users to call 'storm_ack_alerts' after processing to advance the cursor. While it doesn't explicitly state when not to use the tool, the context is clear and the alternative is named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a single canonical event by its Storm slug, including the full set of cross-venue markets attached to that event and each venue's currently published price. Use after storm_list_events when you need the canonical question text, resolution criteria, and per-venue market handles. Sourced from Eyewall Markets / Storm; describes the published-price observation, not a buy or sell recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Storm event slug, e.g. 'will-fed-cut-rates-by-2026-q3'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description implies read-only fetch and adds context that data is observational, not advisory. However, it does not disclose any side effects, auth needs, or limitations beyond that.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two succinct sentences: first defines action and scope, second provides usage guidance and disclaimer. No redundant text; all information is relevant and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input and lack of output schema, the description covers core purpose, usage context, and key outputs (question text, resolution criteria, market handles). Missing exact response structure but acceptable for this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a well-described parameter. The description does not add significant meaning beyond the schema, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a single canonical event by slug, including markets and prices. It distinguishes from sibling 'storm_list_events' by specifying it's for detailed event data after listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly suggests using after storm_list_events and states the need for canonical question text, resolution criteria, and market handles. Includes disclaimer that it's observation, not a recommendation, but does not explicitly mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the canonical Storm view of a single market on a specific venue, including the venue's currently published bid/ask, volume, and the canonical event it's joined to. Use when you have a venue + the venue's native market id (e.g. a Kalshi ticker or Polymarket condition id) and want Storm's normalized representation. Sourced from Eyewall Markets / Storm; describes published price snapshots from the venue's public read endpoints, not a recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| venue | Yes | Venue slug, e.g. 'kalshi' or 'polymarket'. See storm_list_venues. | |
| external_id | Yes | The venue's native market identifier (Kalshi ticker, Polymarket condition id, etc.). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description states it fetches from public read endpoints and is not a recommendation, indicating a safe read operation. Could mention if real-time or cached, but still good.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, no unnecessary words. Efficiently conveys all essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description adequately covers return fields (bid/ask, volume, canonical event) and notes it's not a recommendation. Sufficient for simple two-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear explanations for venue and external_id. The description reinforces the usage but doesn't add significant new constraints or examples beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches the canonical Storm view of a single market, including bid/ask, volume, and associated event. It distinguishes itself from sibling tools like storm_list_events and storm_get_event.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use when you have a venue + the venue's native market id', providing clear conditions. Also references storm_list_venues for obtaining the slug, guiding the agent on prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List canonical prediction-market events (questions/topics) tracked by Eyewall Markets / Storm across the public venues it covers (Kalshi, Polymarket, Manifold, ForecastEx, and others). Use this to discover what events exist before drilling into a specific event with storm_get_event. Supports filtering by category (e.g. 'politics', 'economics') and status (e.g. 'open', 'resolved'), and is cursor-paginated. Read-only market reference data; describes the event ontology, not a recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Page size (1-100). Default server-side is typically 25. | |
| cursor | No | Opaque pagination cursor returned in next_cursor from a previous call. | |
| category | No | Filter to a single category slug, e.g. 'politics'. | |
| status | No | Filter by event status, e.g. 'open' or 'resolved'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description declares 'Read-only market reference data' and mentions cursor-pagination. Lacks details on rate limits or auth, but sufficient for basic safety.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, front-loaded with purpose, each sentence adds value (use case, filtering, pagination, read-only nature). No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description mentions cursor-paginated results. Could specify return fields, but adequate for discovering events before drilling down.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds examples for category and status, but no deeper semantics beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists prediction-market events with specific venues (Kalshi, Polymarket, etc.), uses the verb 'list' and resource 'events', and distinguishes from sibling tool storm_get_event.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this to discover what events exist before drilling into a specific event with storm_get_event', providing a clear use case and alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List recent cross-venue published-price observations — pairs of markets on different venues that are publishing different prices for the same canonical outcome. Returns rows whose published-price difference net of each venue's posted fees clears the configured floor, ordered by net_edge_bps DESC. Sourced from Eyewall Markets / Storm. Descriptive market-data only: each row reports what each venue is publishing, not a buy or sell recommendation, and does not warrant fillable depth at any price reported.
| Name | Required | Description | Default |
|---|---|---|---|
| min_edge_bps | No | Minimum published-price difference net of venue fees, in basis points (1 bp = 0.01%). E.g. 50 returns only rows whose net_edge_bps is >= 50. Defaults to the server-side floor. | |
| limit | No | Page size (1-100). | |
| cursor | No | Opaque pagination cursor returned in next_cursor from a previous call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: lists recent observations, filters by configured floor, orders by net_edge_bps descending, and emphasizes that data is descriptive and non-actionable. This covers safety and operational expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences that front-load the primary purpose in the first sentence and add behavioral caveats in the second. No extraneous words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema), the description covers key aspects: what is returned, ordering, and behavioral constraints. However, it does not mention pagination handling (cursor/next_cursor) explicitly, leaving that to the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by explaining min_edge_bps with an example and mentioning server-side floor default. However, for 'limit' and 'cursor', no additional semantic context is provided beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'cross-venue published-price observations', and distinguishes this tool from siblings like storm_list_events and storm_list_venues by specifying its unique function of identifying pricing disparities between venues.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the tool's purpose and clarifies that it provides descriptive market data only, not buy/sell recommendations or depth warranties. However, it does not explicitly mention when not to use this tool or provide direct alternatives beyond sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all public prediction-market venues covered by Eyewall Markets / Storm, with their slugs, display names, regulatory posture (CFTC-registered DCM, offshore, etc.), posted fee schedules, capability flags (orderbook / AMM / parimutuel), and current ingestion status. Call this first when you need the venue slug to pass to storm_get_market. Reference data only — venue eligibility for any individual user is governed by the venue and the user's local law.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool is reference data only and lists the kind of data returned, which is sufficient for a read-only tool with no side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the action and output, every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description thoroughly enumerates return fields (slugs, display names, regulatory posture, etc.) and clarifies it's reference data, making it complete for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so baseline is 4. The description adds meaning by explaining what the output contains, compensating for the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all public prediction-market venues') and specifies the output fields, distinguishing it from siblings like storm_get_market which needs a slug.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs 'Call this first when you need the venue slug to pass to storm_get_market', providing clear when-to-use guidance and noting that eligibility is governed by venue and local law.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.2storm_ack_alertsstorm_get_alerts_inboxstorm_get_eventstorm_get_marketstorm_list_eventsstorm_list_spreadsstorm_list_venuesScored across 7 tools
Each tool targets a distinct action or resource: acking alerts, polling inbox, fetching events/markets, listing events/spreads/venues. No two tools have overlapping purposes, and descriptions clearly differentiate them.
All tools follow a consistent 'storm_verb_noun' pattern in snake_case, with verbs like ack, get, list. This makes it easy for an agent to infer functionality from names.
7 tools is a well-scoped set for a prediction market data server. Each tool serves a necessary function without unnecessary duplication or gaps, covering discovery, detailed queries, and notification management.
The tool set covers the full lifecycle: discovering events (list), drilling into details (get), accessing markets (get), monitoring spreads and alerts, acknowledging alerts, and listing venues. No obvious missing operations for the stated purpose.
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