Servidor MCP que expone la API de inteligencia de mercados de predicción entre plataformas de Storm a cualquier cliente del Protocolo de Contexto de Modelo (MCP).
Storm es el agente de IA autónomo que opera Eyewall Markets, un servicio de inteligencia de mercados de predicción entre plataformas que abarca Polymarket, Kalshi, Manifold, Futuur, Betfair, ForecastEx y más. Este paquete es un puente MCP ligero de stdio que permite a los clientes LLM — Claude Desktop, Claude Code, Cursor, Zed y cualquier otro host compatible con MCP — leer los eventos canónicos, spreads entre plataformas, catálogo de plataformas y la bandeja de entrada de alertas por usuario de Storm como llamadas a herramientas nativas.
Está destinado a analistas, traders y desarrolladores de agentes que ya tienen una suscripción a Storm y desean que su espacio de trabajo LLM vea lo que Storm ve.
La API de Storm está limitada a los suscriptores del nivel Edge ($499/mes). Genera tu
api_keyen https://eyewallmarkets.com/account. Los niveles inferiores no tienen acceso a la API y recibirán unHTTP 403de cada endpoint que llame este servidor.
Las claves API tienen el formato stk_ seguido de 48 caracteres hexadecimales (52 caracteres en total) y están vinculadas a una sola cuenta. Trátalas como cualquier otra credencial de portador.
La referencia completa de la API se encuentra en https://eyewallmarkets.com/api/docs.
Related MCP server: pmxt-mcp
Edita tu archivo de configuración MCP de Claude Desktop y añade una entrada storm bajo mcpServers:
{
"mcpServers": {
"storm": {
"command": "npx",
"args": ["-y", "@eyewallmarkets/storm-mcp"],
"env": {
"STORM_API_KEY": "stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}La ruta en macOS es:
~/Library/Application Support/Claude/claude_desktop_config.jsonLinux:
~/.config/Claude/claude_desktop_config.json· Windows:%APPDATA%\Claude\claude_desktop_config.json.
Reinicia Claude Desktop. Las siete herramientas storm_* deberían aparecer en la lista de herramientas dentro de cualquier conversación nueva.
claude mcp add storm npx -- -y @eyewallmarkets/storm-mcpLuego exporta la clave API al entorno en el que Claude Code inicia el servidor (o establécela en tu perfil de shell):
export STORM_API_KEY=stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxLa invocación exacta de la CLI puede variar según la versión de Claude Code; consulta la documentación oficial en https://docs.claude.com/en/docs/claude-code/mcp para conocer la forma canónica (incluyendo cómo pasar variables de entorno en la propia línea mcp add).
Después de la instalación, ejecuta /mcp dentro de Claude Code para confirmar que el servidor storm está conectado y que las siete herramientas están registradas.
Cursor lee las definiciones del servidor MCP desde ~/.cursor/mcp.json. Añade la misma estructura utilizada para Claude Desktop:
{
"mcpServers": {
"storm": {
"command": "npx",
"args": ["-y", "@eyewallmarkets/storm-mcp"],
"env": {
"STORM_API_KEY": "stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}Recarga la integración MCP de Cursor (Settings → MCP → Refresh).
Zed configura los servidores MCP bajo assistant.context_servers en ~/.config/zed/settings.json:
{
"assistant": {
"context_servers": {
"storm": {
"command": "npx",
"args": ["-y", "@eyewallmarkets/storm-mcp"],
"env": {
"STORM_API_KEY": "stk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}
}Reinicia Zed o recarga el panel del asistente.
Toda la configuración se realiza mediante variables de entorno leídas al iniciar el servidor.
Variable | Requerido | Predeterminado | Descripción |
| sí | — | Clave API de nivel Edge, formato |
| no |
| Sobrescribe la raíz de la API. Útil para staging o un servidor de desarrollo local. |
| no |
| Tiempo de espera por solicitud en milisegundos. |
| no |
| Uno de |
Las siete herramientas son de solo lectura e idempotentes. Las estructuras de los argumentos se documentan a continuación en una forma similar a JSON-schema; el esquema en vivo es lo que el cliente MCP realmente ve.
storm_list_eventsEnumera eventos canónicos (entre plataformas). Paginado por cursor.
{
"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"] }
}Ejemplo de prompt: "Enumera los próximos 20 eventos políticos abiertos en Storm."
storm_get_eventObtén un solo evento por slug, incluyendo todos los resultados vinculados y los precios por plataforma.
{
"slug": { "type": "string", "required": true, "example": "us-pres-2028" }
}Ejemplo de prompt: "Obtén el registro completo de Storm para us-pres-2028 y dime qué resultado tiene el spread más amplio entre plataformas."
storm_list_spreadsEnumera los spreads recientes entre plataformas donde la ventaja neta (después de comisiones) supera el mínimo. Ordenado por ventaja DESC.
{
"min_edge_bps": { "type": "integer", "default": 100 },
"limit": { "type": "integer", "default": 50, "max": 200 },
"cursor": { "type": "string", "optional": true }
}Ejemplo de prompt: "Muéstrame los 10 mejores spreads de Storm con al menos 250 bps de ventaja ahora mismo."
storm_get_marketBusca un registro de plataforma/mercado único por (venue_slug, external_id).
{
"venue": { "type": "string", "required": true, "example": "polymarket" },
"external_id": { "type": "string", "required": true }
}Ejemplo de prompt: "Busca el mercado de Polymarket 0xabc... en Storm."
storm_list_venuesEnumera todas las plataformas que rastrea Storm, con estado regulatorio (DCM registrado por la CFTC, offshore, etc.), esquemas de comisiones y banderas de capacidad (libro de órdenes, AMM, parimutuel).
{}Ejemplo de prompt: "¿Qué plataformas que rastrea Storm son DCM registrados por la CFTC?"
storm_get_alerts_inboxConsulta la bandeja de entrada de alertas del canal de API del usuario. Los suscriptores de Edge pueden dirigir alertas al canal de entrega api; esta herramienta vacía la cola no confirmada.
{
"since": { "type": "integer", "minimum": 0, "optional": true, "example": 4521 }
}El cursor es el id entero de la última alerta que has visto. Pasa next_since de la respuesta anterior para obtener solo alertas más nuevas; omítelo para leer desde el cursor de confirmación persistente del usuario. Ejemplo de prompt: "Consulta mi bandeja de entrada de Storm y resume todo lo que no he confirmado."
storm_ack_alertsAvanza el cursor de confirmación persistente para que las futuras consultas de la bandeja de entrada omitan las alertas ya manejadas. El cursor está en el lado del servidor y sobrevive a través de las sesiones MCP.
{
"up_to": { "type": "integer", "minimum": 0, "required": true, "example": 4530 }
}Ejemplo de prompt: "Confirma todas las alertas de Storm hasta ahora."
Usuario: Encuéntrame todos los spreads de Storm para las elecciones de 2028 con una ventaja superior a 300 bps y dime los 3 mejores.
Asistente: (llama a
storm_list_spreadsconmin_edge_bps: 300, limit: 50, filtra la respuesta por el prefijo de slug de evento2028_us_presidential_, luego llama astorm_get_eventen los tres mejores para enriquecer)Devuelve los tres spreads de las elecciones de 2028 más amplios actualmente abiertos, el par de plataformas en cada lado y qué plataforma tiene la opción barata.
Usuario: Consulta mi bandeja de entrada de Storm y resume las alertas no confirmadas, luego confirma todo lo que resumiste.
Asistente: (llama a
storm_get_alerts_inboxsinsince, resume los elementos por categoría, luego llama astorm_ack_alertsconup_toestablecido en elidmás grande que vio)Devuelve un resumen categorizado de alertas no confirmadas (cruces de spread-edge, cambios de estado de plataforma, eventos de resolución) y confirma el avance del cursor para que las consultas posteriores solo devuelvan elementos nuevos.
La API de Storm impone 10 solicitudes por segundo por clave API en el lado del servidor. Si excedes eso, el servidor devuelve HTTP 429 con Retry-After: 1. Este puente MCP no reintenta automáticamente; muestra el error al LLM como contenido de texto plano en el resultado de la herramienta para que el modelo pueda decidir si retroceder, reintentar o rendirse.
Los errores se devuelven al LLM en esta forma (contenido de texto en el resultado de la herramienta, 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_credentialsLos fallos a nivel de red (DNS, TCP, TLS, tiempo de espera) se muestran como:
Storm API error (HTTP 0, transport): request timeoutEl estado HTTP completo, el código de error de Storm y el mensaje humano siempre se incluyen para que el LLM pueda actuar sobre ellos.
git clone https://github.com/lsudduth/storm-mcp.git
cd storm-mcp
npm install
npm testApunta a un servidor de desarrollo local de Storm sobrescribiendo la base de la API:
STORM_API_BASE=http://localhost:8080/api/v1 \
STORM_API_KEY=stk_dev_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx \
node src/index.mjsEl servidor habla MCP stdio, por lo que puedes manejarlo directamente con cualquier arnés de prueba MCP o canalizando marcos JSON-RPC manualmente.
Las pruebas utilizan el ejecutor de pruebas integrado de Node; sin Jest, sin Vitest, sin transpilador.
MIT — ver LICENSE.
© 2026 XCH1TB, LLC dba Eyewall Markets.
Storm en sí mismo — el agente de IA autónomo que opera Eyewall Markets y produce los datos que este servidor expone — es una base de código interna separada. Este paquete es solo el puente MCP orientado al cliente hacia la superficie de la API pública de solo lectura de Storm.
Este software y los datos que muestra se proporcionan solo con fines informativos. Nada de lo devuelto por Storm o por este servidor MCP constituye asesoramiento legal, financiero, fiscal o de inversión. La participación en mercados de predicción está sujeta a tus leyes locales y a las propias reglas de elegibilidad de la plataforma; en particular, Polymarket está restringido en los Estados Unidos bajo órdenes de la CFTC y no está disponible para la mayoría de las personas estadounidenses. La elegibilidad de la plataforma es tu responsabilidad, no de Storm ni de este servidor. Storm agrega el estado del mercado observable públicamente y no realiza operaciones en tu nombre.
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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