get_government_contractsRecent notable US federal contract awards — useful for tracking which public companies are winning government money.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum rows to return (1-25). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds that the tool returns 'recent notable' awards, implying a filtered subset, which is useful behavioral context. It does not describe return format, pagination, or any system-side effects, but given the annotations, this is adequate.
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, well-structured sentence that front-loads the core purpose and appends a use case via an em dash. Every word earns its place with no redundancy or fluff.
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?
The tool is simple with one parameter and no output schema. The description states the data content and a use case, but it lacks specifics like what fields are returned (e.g., company name, award amount) or what 'notable' means. Given the absence of an output schema, a bit more detail would make it complete.
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 coverage for the single parameter 'limit' is 100%, so the schema already documents the parameter with type, default, and range (1-25). The description adds no additional parameter semantics, but the baseline of 3 applies because the schema carries the full burden and the description does not introduce confusion.
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 'recent notable US federal contract awards' and frames it as useful for tracking public companies winning government money. While it lacks an explicit verb like 'list' or 'retrieve', the noun phrase combined with the tool name makes the purpose clear. It distinguishes from siblings like get_congress_trades by focusing on contracts rather than congressional trades.
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 provides a clear use case: 'useful for tracking which public companies are winning government money.' This gives the agent context for when to select this tool. However, it does not explicitly mention alternative tools or conditions when this tool should not be used, so it falls short of a 5.
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.
With 40 tools, many share overlapping domains: get_smart_money_flow vs get_smart_money_inflow, scan_launchpads vs get_launchpad_radar, track_whales vs get_hyperliquid_whales, and check_token_safety vs analyze_token_security. The detailed descriptions help, but the boundaries are not always clear, making misselection likely.
The tool names employ a wide variety of verbs (get_, analyze_, scan_, track_, find_, generate_, recommend_, run_, list_, ask_, assess_, detect_) with no consistent pattern. While all use snake_case, the inconsistent verb choices and occasional deviations like forecast_chart prevent predictability.
40 tools is well above the typical 3-15 well-scoped range and exceeds the 25+ threshold for 'too many'. While the broad 'crypto intelligence' purpose justifies some breadth, the sheer number makes the surface unwieldy and suggests a lack of focused scoping.
The tool set covers a wide range of crypto intelligence domains: market analysis, forecasting, whale tracking, yield/arbitrage, security checks, prediction markets, backtesting, and even content generation. Missing operations are minor (e.g., no direct portfolio management), but core analysis and data retrieval workflows are well represented.