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Glama

Backtesting Arena

by io.github.Schoasch

Get Knowledge Object

arena_get_knowledge

What does the platform know about this subject? Fetch a versioned, explainable Knowledge Object by type + subject (e.g. type='market_regime', subject='GLOBAL'). Returns the current published envelope: payload, explanation (factors + weights + confidence), provenance (inputs + params), ontology binding, compute version. ONE tool covers ALL knowledge types. Set include_graph=true to also walk the knowledge graph: resolved outbound edges (what this object is derived_from / references) + inbound edges (what derives from / references it), each with api_path + seo_slug so you can follow them. [Free tier; per-object access additionally gated by min_tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesKnowledge object type, e.g. 'market_regime'.
as_ofNoSpecific date YYYY-MM-DD. Omit for latest.
subjectYesSubject ref, e.g. 'GLOBAL', 'BTC'.
include_graphNoIf true, attach the resolved edge neighbourhood (outbound + inbound) for graph traversal.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / context
      Removed 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"
-}
    • changedInput schema / required
      Previous value: -[
-  "type",
-  "subject",
-  "context"
-]New value: +[
+  "type",
+  "subject"
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden. It discloses the exact return envelope (payload, explanation, provenance, ontology binding, compute version), notes the objects are versioned and published, and explains the include_graph extension with edge details (api_path + seo_slug). It also mentions per-object access gating by min_tier. These are substantive behavioral disclosures beyond a simple 'fetch' description, though it omits error or rate-limit behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-organized: starts with a framing question, then purpose, response envelope, scope ('ONE tool'), graph extension, and access note. The rhetorical opener adds flavor but every following sentence delivers concrete information. It is slightly long but efficiently structured with key details front-loaded.

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?

Comprehensive for a tool with no output schema. It specifies the full return envelope fields, the graph traversal behavior with edge attributes, and the access gating condition. The scope clarification and examples ensure an agent can call it correctly without needing an output schema. All essential information for successful invocation is present.

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 description coverage is 100%, so the baseline is 3. The description adds practical value by giving example values for type and subject, clarifying that as_of defaults to the latest when omitted, and explaining the effect of include_graph on graph traversal. These clarifications go beyond the schema's brief property descriptions, making the parameters more actionable for an agent.

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?

States a specific verb 'Fetch' and resource 'Knowledge Object' by type and subject, with concrete examples (type='market_regime', subject='GLOBAL'). The phrase 'ONE tool covers ALL knowledge types' clearly differentiates it from the many specific arena_get_* siblings, establishing that this is the general knowledge retrieval tool. The description is unambiguous and immediately scopes the operation.

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?

Provides clear context by stating it covers ALL knowledge types, which implies it is the general-purpose alternative to the numerous type-specific getters (e.g., arena_get_macro_regime, arena_get_btc_market_structure). It gives practical examples and explains the optional include_graph for graph traversal. However, it does not explicitly enumerate when-not-to-use cases or name specific alternatives, so the guidance is strong but not fully exhaustive.

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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TDQS

A3.6/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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'.

Tool Count2/5

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.

Completeness4/5

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.

Resources