arena_list_knowledgeWhat knowledge objects exist here? Discover what Knowledge Objects exist: lists all published types + their subjects (with min_tier, api_path, seo_slug, latest as_of). Use this BEFORE arena_get_knowledge to learn valid type/subject pairs instead of guessing. New types appear automatically. [Free 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 provided, the description carries the full burden. It states it's a listing operation (implying read-only) and notes the free tier, giving cost context. It does not explicitly declare side effects or rate limits, but the nature of a zero-parameter list operation and the detailed output fields make behavior sufficiently clear.
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 front-loaded with the core purpose, followed by usage guidance and a note about free tier. It is a bit conversational ('What knowledge objects exist here?') but each sentence earns its place, with no redundancy. Length is appropriate for the information conveyed.
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 parameterless list tool, the description is complete: it states what is returned, when to use it, and cost implications. It does not describe the exact response format or pagination, but these are less critical for a simple listing. The absence of an output schema is mitigated by listing the returned fields.
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 schema coverage is trivially 100%. The baseline for 0 params is 4. The description adds value by explaining the output contents (types, subjects, and the specific fields), which compensates for the lack of an output 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 lists all published knowledge types and their subjects, with specific fields (min_tier, api_path, seo_slug, latest as_of). It distinguishes itself from arena_get_knowledge by positioning itself as a discovery step, making its purpose unambiguous.
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?
Contains an explicit directive: 'Use this BEFORE arena_get_knowledge to learn valid type/subject pairs instead of guessing.' It also notes that new types appear automatically, implying the tool should be re-run to stay current. This clearly tells the agent when and why to use it.
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.