Where a specific job offer sits against advertised pay for a role in a state: percentile vs live posting ranges, with an honest fallback to national data when the state sample is under 25 disclosed postings. Advertised ranges are not settled offers.
| Name | Required | Description | Default |
|---|---|---|---|
| role | Yes | Role, e.g. 'RN', 'BCBA' | |
| basis | Yes | Pay basis of the offer | |
| state | Yes | Two-letter US state code | |
| amount | Yes | Offer amount, e.g. 42.5 (hourly) or 78000 (annual) |
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?
The description discloses the fallback to national data when the state sample has fewer than 25 disclosed postings and warns that advertised ranges are not settled offers. This adds meaningful behavioral and data-limitation context beyond the schema. It stops short of describing the exact response format, but the core behavior is transparent.
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 that front-loads the main comparison and then adds the fallback rule and caveat. There is no fluff, though the phrasing 'where a specific job offer sits' is slightly indirect.
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 no annotations and no output schema, the description provides substantial context: comparison logic, percentile basis, fallback threshold, and a data-quality caveat. It could mention return value shape or explicitly route to get_pay_range for raw ranges, but an agent has enough to invoke the tool correctly.
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 input schema already provides full descriptions for all four parameters, including amount examples, so schema coverage is complete. The description reinforces the role/state/amount relationship but does not add parameter-level semantics beyond what the schema already conveys.
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 operation: compare a specific job offer to advertised pay and report its percentile. It names the resource (job offer vs live posting ranges) and role/state scope, which distinguishes it from list_employers. It does not explicitly differentiate from get_pay_range, but the core purpose is unambiguous.
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 use case is implied: call this when you have a specific offer amount and want to position it against market postings for the same role and state. The description gives no explicit when-not-to-use guidance and does not mention alternatives such as get_pay_range for raw range lookups.
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.