report_historyWeekly net-position time series for one futures market (non-commercial net, commercial net, open interest), most recent first. Pass the exact market_and_exchange_names from search_markets. Useful for trend / sentiment shifts over time. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| weeks | No | Number of weekly reports to return (default 26, max 104). | |
| market_name | Yes | Exact market_and_exchange_names from search_markets. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "market_name": "GOLD - COMMODITY EXCHANGE INC."
+ },
+ {
+ "market_name": "E-MINI S&P 500 - CHICAGO MERCANTILE EXCHANGE",
+ "weeks": 52
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral details beyond annotations: 'most recent first' (ordering), 'Pass the exact ... from search_markets' (dependency on another tool), and 'Keyless' (authentication status). These details are not captured by the readOnlyHint, idempotentHint, or other annotations, enriching transparency.
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 consists of four short, purposeful sentences: output description, input requirement, use case, and authentication. No wasted words, and the information is front-loaded with the core functionality. Every sentence earns its place.
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?
Given two parameters and no output schema, the description covers output type (time series with specific fields), input requirement (market name from sibling), ordering, use case, and authentication. It does not detail error handling or exact output format, but these are not critical for this straightforward tool.
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 schema already has 100% coverage with descriptions for both parameters. The description adds context by reiterating the market_name source (search_markets) and implying the effect of weeks via 'most recent first' and time series nature. This adds some semantic value beyond the 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 returns a 'weekly net-position time series' with specific fields (non-commercial net, commercial net, open interest) and ordering (most recent first). It implies a verb like 'retrieves' or 'gets', making the purpose clear. It is distinct from siblings like search_markets, but does not explicitly differentiate from other data retrieval tools.
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 advises using the exact output from search_markets for the market_name parameter and states the tool is 'useful for trend / sentiment shifts over time.' However, it does not specify when to avoid this tool or mention alternatives among the many sibling tools, limiting its guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.