Advertised pay range for a healthcare role in a US state, computed weekly from employers' own live job postings (never surveys). Returns hourly and salary ranges separately, sample sizes, city-level ranges where available, and honest labels when data is thin.
| Name | Required | Description | Default |
|---|---|---|---|
| role | Yes | Role, e.g. 'RN', 'BCBA', 'physical therapist', 'financial analyst' | |
| state | Yes | Two-letter US state code, e.g. NY |
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 no annotations, the description carries the full burden, and it does well by disclosing the data source (live job postings), freshness (computed weekly), a negative constraint (never surveys), and what it returns. It also mentions honest labels for thin data, though it does not fully specify what those labels look like.
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 dense sentence with no filler. It front-loads the core purpose, then packs in the key behavioral details and return-value caveats without wasting words.
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 only two simple parameters, full schema coverage, and no output schema, the description is largely complete: it explains what is returned, how the data is computed, and how thin data is handled. A slightly more explicit note about the format of the returned ranges would make it fully 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 description coverage is 100%, so the parameters role and state are already well documented. The description adds useful context like 'healthcare role' and two-letter state codes, but it does not materially extend the schema's parameter explanations.
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 states a specific action and resource: retrieving an advertised pay range for a healthcare role in a US state. It also differentiates the tool from siblings like check_offer and get_employer_score by focusing on market-wide pay ranges rather than individual offers or employer scoring.
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 clear context for when to use this tool: whenever an agent needs current advertised pay ranges for a healthcare role in a state. It does not explicitly state when not to use it or name alternative tools, but the intended use case is unmistakable.
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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Each tool targets a distinct operation: comparing an offer, retrieving a pay range, getting an employer score, and listing employers. There is mild overlap between check_offer and get_pay_range, but their purposes are clearly separated by whether a specific offer is being evaluated.
All tool names follow the same verb_noun pattern: check_offer, get_employer_score, get_pay_range, list_employers. The naming is predictable and makes the action and target of each tool immediately clear.
Four tools is a well-scoped count for this focused domain. Each tool covers a meaningful interaction with the pay transparency dataset without redundancy or unnecessary surface area.
The set covers the core workflows: viewing pay ranges, checking an offer, seeing an employer's transparency score, and browsing tracked employers. Minor gaps exist, such as no dedicated employer detail view or filtering/searching, but agents can accomplish the main jobs without dead ends.