Determine whether a job is on the Treasury Tipped Occupation list (Treas. Reg. § 1.224-1, final Apr 2026) for the § 224 'no tax on tips' deduction. Fuzzy-matches the job name; returns the official listing (name, TTC code, category) or a definitive 'not listed'.
| Name | Required | Description | Default |
|---|---|---|---|
| job | Yes | e.g. "bartender", "software engineer", "DJ" |
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. It discloses the fuzzy-matching nature and the return value (official listing or 'not listed'). It does not mention side effects or permissions, but as a read-only query, this is acceptable.
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 two concise sentences. It front-loads the purpose and regulatory reference, then details the behavior. No wasted 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 the low complexity (one parameter, no output schema), the description is complete. It explains the input and output sufficiently. No additional context like rate limits is expected for such a 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 input schema covers 100% of parameters with examples. The description adds value by explaining fuzzy matching on the job name, which goes beyond the schema's static description.
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: checking if a job is on the Treasury Tipped Occupation list for the §224 deduction. It specifies the regulation, the fuzzy-matching behavior, and the output format, distinguishing it from sibling tools that focus on calculations or other lookups.
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 explains when to use the tool (to determine tip deduction eligibility) and implies it's a lookup. It does not explicitly mention when not to use it or list alternatives, but the context of sibling tools provides implicit guidance.
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.
Most tools are cleanly separated by entity type or operation, and the calculate_* tools explicitly route to each other (individual vs business vs fiduciary). The main tension is between calculate_tax and compute_return, which share inputs and both produce federal outcomes, and between verify_fact and verify_tax_claim, though the descriptions do clarify the answer-vs-line-set and law-vs-amount distinctions.
Every tool name follows a snake_case verb_noun pattern, and the noun clearly identifies the object (tax, return, dependent, rule, fact, cliff). The only mild quibble is calculate vs compute being synonyms, but the pattern is otherwise uniform and predictable.
15 tools is at the upper edge of the ideal range but each one has a distinct role: computation, return composition, dependency tests, rule lookup, verification, and specialized analyses. The count feels proportionate to a tax engine that must both calculate returns and explain/verify the law behind them.
The surface covers individual, business, and fiduciary federal tax; complete Form 1040 and multi-state return composition; dependency determination; rule search/explanation; parameter lookup; and fact/claim verification. For the apparent purpose of an auditable tax oracle, there are no obvious dead ends or missing core operations.
