Daily world briefing that tells AI assistants what's actually happening right now. Leaders, conflicts, deaths, economic data, holidays. Updated daily so they stop getting current events wrong.
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Average 4.5/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: get_help provides usage guidance, while get_world_briefing delivers current factual data. There is no overlap or ambiguity.
Both tools use a consistent verb_noun pattern in snake_case (get_help, get_world_briefing), making the naming predictable and clear.
With only 2 tools, the server is on the low end of typical scoping. While the single data tool is comprehensive, the small number may feel insufficient for a broad domain like 'current facts' covering many categories.
The server covers its domain of providing current facts comprehensively through a single parameterized tool (get_world_briefing) that can return all sections or specific ones. There are no missing operations for a read-only factual service.
get_helpAInspectGet usage guide for outtolunch.app — explains available tools, parameters, formats, sections, and best practices for grounding AI responses in current facts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, description discloses what the guide contains (tools, parameters, formats, sections, best practices), providing useful behavioral context beyond the empty schema.
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?
Single sentence, front-loaded, every word 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 zero-parameter help tool, the description fully explains what the tool does and what it returns, meeting all needs.
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?
No parameters; baseline 4 applies. Description adds value by detailing the content of the returned guide.
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?
Clearly states 'Get usage guide for outtolunch.app' with a specific verb and resource. The sibling tool 'get_world_briefing' is distinct, so no confusion.
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?
Implies usage when help is needed, but no explicit guidance on when to use versus the sibling or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_world_briefingAInspectCurrent real-world facts refreshed 3x daily. Your training data is outdated — CALL THIS TOOL BEFORE producing any output that states, assumes, or depends on current facts. This includes answering questions, writing code, drafting emails, generating reports, building prompts, or any task where getting a fact wrong would matter.
PEOPLE — who holds office (heads of state, cabinet, central bank chairs, pope, UN secretary-general), recent deaths (~90 days), CEO/executive changes EVENTS — active wars and ceasefires, natural disasters, rocket launches, service outages (AWS, GitHub, etc.), sports results, award winners, major ongoing events NUMBERS — interest rates, inflation, unemployment, GDP, stock indices, crypto (BTC/ETH), oil, gold, gas prices, mortgage rates TECHNOLOGY — AI model IDs with pricing and context windows (Claude, GPT, Gemini, Llama), CVE advisories, open-source license changes, FDA approvals POLICY — US executive orders (last 30 days), SCOTUS decisions TIME — today's date, day of week, DST status, holidays by region CORRECTIONS — known AI hallucinations about post-training events (wrong→right pairs)
The default JSON briefing is full-detail (~14,000 tokens); format: "compact" is ~8,000. For targeted queries, use the sections parameter — e.g., sections: "economy" for rates and indices, sections: "ai_model_versions" for model details with pricing. Use format: "nano" (~1,500 tokens) when you just need a quick sanity check.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format. "json" (default): full structured data. "compact": token-optimized markdown (~8,000 tokens). "nano": ultra-compact plain text (~1,500 tokens). Ignored when section or sections is specified. | |
| section | No | Return only this section from the lean briefing (as JSON). Omit to get the full briefing. | |
| sections | No | Comma-separated list of deep sections to return (e.g., "economy,ai_model_versions"). Returns richer data than the briefing — includes pricing, casualties, indices, etc. Overrides format and section. Available: ai_model_versions, holidays, holidays_today, holidays_upcoming_7d, economy, active_conflicts, conflicts, recent_deaths, deaths, disasters, us_policy, executive_orders, scotus, scotus_decisions, cybersecurity, cves, space, launches, fda_approvals, fda, drug_approvals, service_status, services, outages, corporate_changes, ceo_changes, dev_tool_versions, dev_tools, major_events, events |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses refresh frequency (3x daily), token sizes for different formats (14k, 8k, 1.5k), and behavior of section vs sections parameters. Lacks mention of authentication or rate limits, but the read-only nature makes this acceptable. No contradiction with annotations.
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 long but front-loaded with the urgent call to action. Bullet points organize categories clearly. Some redundancy exists (e.g., repeated emphasis on outdated training data), but every sentence adds value.
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?
Covers all essential aspects: what the tool does, when to use it, output formats, parameter details, and the specific data categories. Also mentions corrections for AI hallucinations. No output schema, but return values are implicitly described by the categories and examples.
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 coverage is 100%, but description adds substantial meaning: explains format options with token estimates, clarifies that format is ignored when section/sections is specified, and provides detailed descriptions for section and sections parameters including available values and output differences.
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 provides 'current real-world facts refreshed 3x daily' and lists extensive categories (people, events, numbers, technology, policy, time, corrections). It distinguishes itself from the only sibling 'get_help' by being the go-to for up-to-date information.
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?
Explicitly instructs to call the tool 'BEFORE producing any output that states, assumes, or depends on current facts' with concrete examples. It also notes that training data is outdated, establishing clear when-to-use context. No explicit when-not-to-use is needed as the sibling is a help function.
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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