arena_check_subscription_updatesHas anything I subscribed to fired? Returns all undelivered updates for the API key, then marks them as delivered. Call regularly to consume the polling queue. Updates contain payload with subscription_type, current value, previous value, and trigger context. [API Pro tier]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / contextRemoved value: -{
- "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
- "type": "string"
-}Input schema / requiredRemoved value: -[
- "context"
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It clearly states the destructive side effect (marks updates as delivered), the return payload structure (subscription_type, current value, previous value, trigger context), and the polling cadence. This is strong transparency for a tool with zero annotations, though it doesn't mention error cases or empty-queue behavior.
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 short sentences pack the purpose, side effect, polling advice, and payload details with zero redundancy. The opening question sets immediate context, and each sentence earns its place. This is an exemplary model of concise, front-loaded tool descriptions.
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 contract with no parameters and no output schema, the description covers the essential points: what it returns, the side effect, usage cadence, and payload shape. It lacks explicit handling for the empty case, but that is reasonable to infer. The tier restriction is also disclosed. This is nearly complete for an agent to call it correctly.
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 the description cannot add parameter-level detail. It correctly infers the API key is passed via header/context rather than a parameter. With nothing to document, this receives the baseline score of 4.
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 immediately answers 'Has anything I subscribed to fired?' and specifies exactly what the tool does: returns undelivered updates for the API key and marks them delivered. It clearly distinguishes from siblings like arena_list_subscriptions (which lists active subscriptions) and the many data-retrieval getters, making its unique role as the subscription-update poller unmistakable.
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 says 'Call regularly to consume the polling queue,' giving clear usage context. It doesn't explicitly state alternatives or exclusions, but since no sibling tool performs this function, the guidance is sufficient. The 'API Pro tier' note also warns about access constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools cover overlapping market indicators (e.g., cycle state, pulse, bullmarket ampel, volatility phases) and several share similar get_*_history patterns, which could cause an agent to select the wrong one. However, each tool has detailed descriptions with explicit references to related tools to reduce ambiguity.
Tool names generally follow a verb_noun pattern (arena_get_*, arena_list_*, arena_run_*, arena_subscribe_*), but there are inconsistencies such as 'validate_strategy' (no arena_ prefix), 'arena_status' (not a clear verb_noun), and variations like 'arena_run_grid_backtest' vs 'arena_run_backtest'.
With 84 tools, the server is heavily over-scoped for a typical MCP server, likely causing navigation and selection overhead. While the domain (crypto backtesting and analytics) is broad, 84 tools exceed reasonable coherence and impose a high cognitive load.
The tool surface covers a wide range of analytics (market indicators, backtesting, validation, alerts, subscriptions, reports) with few obvious dead ends. Minor gaps exist like lack of direct portfolio management or strategy editing, but core workflows are well covered.