RUNS WITHOUT AN API KEY — call it right now to check us before signing up for anything. What Meridian Trace actually holds: every source registry, the market it covers, how many registrations are on file from it, and when it was last crawled. Call this to verify coverage and freshness for yourself before relying on other tools, or to answer "do you cover market X, and how current is it?". No arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full behavioral disclosure burden. It does a decent job: it states that the tool runs without an API key, takes no arguments, returns the actual coverage data of what Meridian Trace holds, and emphasizes freshness/currentness. It doesn't describe failure modes or output formatting, but for a simple, safe, read-only query it provides meaningful behavioral 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 is a little wordy with promotional phrasing like 'call it right to check us before signing up for anything', but it front-loads the key behavioral fact (no API key) and then clearly lists the returned data. Most sentences earn their place; only slight redundancy prevents a 5.
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 description is complete enough for a tool with no parameters, no output schema, and no annotations. It explains the exact data it returns, address no-auth behavior, and provides concrete usage intent. An agent can successfully decide when to call it and what to expect from it.
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?
There are zero parameters, so the schema already fully describes the input surface. The description redundantly confirms 'No arguments', which is helpful but adds no new parameter information. With no parameters to document, the baseline score of 4 is appropriate.
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 what the tool does: it returns the current coverage of Meridian Trace — every source registry, market, number of registrations, and last-crawl time. It is transparent about this being a no-argument query. However, it never distinguishes itself from the very similar sibling get_market_coverage, so an agent cannot fully separate the two based on purpose alone.
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 gives strong when-to-use guidance: 'call it right now' to verify coverage and freshness, 'before relying on other tools', and to answer questions like 'do you cover market X, and how current is it?'. It does not explicitly say when to use a sibling instead, such as get_market_coverage, so exclusion/routing is incomplete.
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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The tools cover clearly different queries—classification, predicate lineage, similar devices, market presence, registration details, timelines, recent entries, license holders, and coverage—so most are easy to distinguish. However, the get_registration family and get_coverage/get_market_coverage share naming patterns that could cause initial misselection despite thorough descriptions.
All tool names follow a snake_case verb-first pattern, which is consistent and readable. The main weakness is that get_coverage and get_market_coverage are name-level near-duplicates, and the registration variants require careful reading to tell apart.
Eleven tools is well within the ideal range for a specialized regulatory data domain, and each tool addresses a distinct part of the workflow. None feel redundant or superfluous, and the set is small enough for reliable agent routing.
The domain is covered broadly: manufacturer resolution, registration list/detail, market presence, timelines, recent activity, license holders, classification, US predicate history, and competitive devices are all present. Minor gaps such as a direct global device-type search across all manufacturers or bulk export can be worked around with existing tools.
