get_correlationsCross-asset correlations by effect type.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| effect_type | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile: readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds the effect-type scoping dimension. It does not disclose behavior like default handling when effect_type is null or how limit applies, but the annotation coverage lowers the burden for this dimension.
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 a single focused phrase with no filler, and the core subject is front-loaded. It is almost too terse, but every word contributes meaning, so it earns a high conciseness score despite being under-specified.
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?
While annotations and the output schema cover safety and return structure, the description leaves critical invocation details unresolved, especially the meaning of effect_type and the behavior of limit. For a tool with zero schema description coverage, this is a meaningful gap.
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 0%, and the description only hints that effect_type is relevant to the correlation grouping; it does not define valid values or behavior. The limit parameter is not addressed at all, leaving an agent without enough information to invoke the tool confidently.
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 identifies a specific resource—cross-asset correlations—and this distinguishes it from the many other get_* siblings. However, it is a noun phrase rather than a full statement of capability, and 'effect type' is left undefined, so it falls short of a 5.
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?
There is no guidance on when to use this tool versus alternatives, nor any mention of intended scenarios, exclusions, or related tools. The phrase 'cross-asset correlations' weakly implies a use case, but the description does not actively help an agent choose between this and similar data-retrieval tools.
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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Most tools are distinct, but several pairs overlap in purpose: get_market_data and list_watchlist both return watchlist prices/signals, and the four 'brief' tools have fuzzy boundaries. Descriptions help, but an agent could easily select the wrong one without deeper context.
The dominant verb_noun pattern (get_* for details, list_* for collections) is clear and consistent in style. There are minor deviations like lookup_index_by_brand and run_technical_analysis, and several plural collections use get_, but these remain readable and mostly predictable.
39 tools is well past the 25+ threshold and represents a heavy surface for an agent to navigate. The broad wealth domain explains some of the count, but many granular getters could be consolidated or curated.
The set covers the advertised wealth surface well—portfolio, risk, credit, real assets, markets, geopolitics, watchlist, goals, family, and documents—but it is read-only and lacks obvious navigation endpoints like list_portfolios or detail views for goals/alerts. Agents can retrieve most data, but not all lifecycle operations or entity enumerations are present.