Top-5 front-running signals (policy event + trade spike 30d prior). Updated weekly Mon 07:00 UTC. Returns signal_id, policy_date, hs_code, spike_multiple, confidence. FREE (lead magnet, public — signals delayed 7 days from detection).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max signals to return (default: 5, max: 50) | |
| since | No | Filter signals on/after date, YYYYMMDD |
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?
Since no annotations are provided, the description carries the full behavioral disclosure burden. It does well by revealing update cadence (weekly, Monday 07:00 UTC), data recency (signals delayed 7 days from detection), access level (free/public), and the exact output fields. This adds meaningful context beyond the schema.
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 compact and front-loaded with the core purpose, then adds cadence, output fields, and access constraints. Every clause carries useful information and there is no redundant or filler language.
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?
For a simple tool with two optional parameters and no output schema, the description covers the input basis, the returned fields, freshness, and access model. Minor gaps remain, such as confidence scale interpretation and whether 'limit' can exceed the implied top-5, but these are not critical for basic invocation.
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 100%, with both 'limit' and 'since' already documented including defaults and format. The description adds no parameter-specific semantics, so the baseline score of 3 is appropriate because the schema handles the heavy lifting.
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 identifies the resource: top-5 front-running signals based on a policy event and trade spike 30 days prior. It also lists the returned fields, giving a concrete sense of what the tool produces. It does not explicitly compare itself to siblings like prediction_signals or wave_signal, but the domain and output are specific enough to avoid major ambiguity.
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 implies when to use the tool: when the agent needs publicly available front-running signals, especially those delayed by 7 days and updated weekly. It provides useful access context ('FREE lead magnet, public') but does not state when not to use it or name alternative sibling tools for similar signal needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.