storm-mcp

Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count5/5
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.
Completeness5/5
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.
Average 4.5/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters5/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior5/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.

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