Get the organization's current email branding (display name, logo URL, brand color, intro, business address, reply-to).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| branding | No | The stored branding row, or null when the organization has never set branding. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and destructiveHint=false, so no safety contradiction exists. The description adds useful context by specifying the exact branding properties involved and that the values are the organization's current ones, though it does not discuss auth requirements or edge cases like missing branding.
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?
A single sentence with no filler, the verb and resource are front-loaded, and the parenthetical field list is compact and informative. Every element of the description earns its place.
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 no-parameter read-only tool with an output schema and safety annotations already present, the description is fully sufficient. It identifies the resource scope (organization), operation (get), and meaningful content fields, leaving no material gap for an agent to call it 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 tool accepts zero parameters, so the baseline of 4 applies; the description correctly avoids explaining nonexistent parameters. The field list serves as a form of return-value context rather than parameter semantics.
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 uses the specific verb 'Get' with the resource 'email branding' and enumerates six concrete fields (display name, logo URL, brand color, intro, business address, reply-to). It clearly distinguishes from the sibling set_email_branding by framing this as a read of the current state.
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 phrase 'Get the organization's current email branding' gives clear retrieval context, and the word 'current' implies this is for reading existing settings rather than changing them. However, it does not explicitly mention when not to use it or name set_email_branding as the alternative for modifications.
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.
Many tools occupy closely related decision and rate-lookup spaces—get_financing_decision, rate_sanity_check, compare_products, batch_decisions, and simulate_decision can all appear relevant to a shopping question. The descriptions work hard to disambiguate them, and some pairs are explicitly cross-referenced, but the boundary between tools like create_monitor and create_competitor_monitor or rate_sanity_check and check_competitive_position still requires careful reading.
The set mostly follows a clear snake_case verb_noun pattern: create_*, update_*, delete_*, get_*, list_*, scan_*, simulate_*, send_*. Minor deviations like max_loan, rate_sanity_check, and refinance_break_even break the verb-led convention, and get_ vs list_ is used somewhat loosely, but the overall pattern remains predictable.
At 80 tools, this is an extremely large surface for a rates API. Even though the server spans rate data, financing decisions, membership eligibility, email campaigns, monitors, digests, and documentation, 80 tools is excessive and will overwhelm agents with selection overhead.
The core domain is covered thoroughly: raw rate lookup, benchmarks, history, financing decisions, affordability, refinance analysis, eligibility, campaigns, monitors, cohorts, scenarios, and shares all have lifecycle support. Minor gaps exist—there is no update_cohort or update_scenario, and raw deposit-rate listing is less explicit than mortgage/auto rate listing—but agents can work around these.
