Generate the approval prompt and spend policy an agent should show before using a paid or risky tool. Returns maxPayment, seller checks, risk reasons, allowed fields, and what not to send.
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | ||
| price_usd | No | ||
| route_url | No | ||
| tool_name | No | ||
| task_intent | No | ||
| tool_schema | No | ||
| seller_address | No | ||
| max_payment_usd | No |
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?
With empty annotations, the description carries the full burden. It discloses the return content ('maxPayment, seller checks, risk reasons, allowed fields, and what not to send') but does not state whether the tool is read-only, has side effects, requires auth, or any other behavioral traits. This is a significant gap for a tool with no annotation support.
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 sentence that front-loads the purpose and lists return items. It is reasonably concise and contains no fluff, though the list of returns makes it slightly dense. It 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?
The tool has 8 parameters with no schema descriptions, no output schema, and no annotations. The description only provides a purpose and a high-level return list. It lacks parameter semantics, usage guidance beyond a single clause, and any behavioral details. This is severely incomplete for such a parameter-rich 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 has 0% description coverage, and the description does not explain any of the 8 parameters. It only mentions return fields. With no parameter-level information provided in the description, the agent cannot infer how to set parameters like 'method', 'price_usd', or 'tool_schema'. The description completely fails to compensate for the schema gap.
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's function: 'Generate the approval prompt and spend policy an agent should show before using a paid or risky tool.' This is a specific verb+resource. It does not explicitly name sibling tools, but its purpose is distinct enough from the sibling list, so it gets a 4 rather than 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?
The description provides a clear usage context: 'before using a paid or risky tool.' It does not explicitly mention alternatives or when not to use the tool, but the context is evident. This meets the bar for 'clear context, no exclusions'.
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.
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.