Out to Lunch
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
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.
Available Tools
2 toolsget_helpAInspect
Get 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 | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It adequately establishes that the tool performs a read-only fetch of instructional content covering specific domains (tools, parameters, formats, sections). However, it omits behavioral specifics such as response format (JSON vs. markdown), payload size characteristics, or idempotency that would fully characterize the operation.
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, efficiently structured sentence employing an em-dash to separate the action clause from the content enumeration. Every listed element ('available tools', 'parameters', 'formats', 'sections', 'best practices') adds distinct informational value about the guide's scope without redundancy or tangential information.
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 absence of an output schema, the description adequately compensates by enumerating the specific informational domains covered in the guide. For a zero-parameter metadata tool of this simplicity, detailing the instructional content (including the grounding purpose) provides sufficient context for an agent to decide when to invoke it, though an explicit note on return type would further improve completeness.
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 contains zero parameters, which per rubric establishes a baseline score of 4. The description appropriately does not invent input parameters, though it mentions 'parameters' only in the context of what the returned guide explains (other tools' parameters), not as inputs to this tool.
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 clear resource 'usage guide for outtolunch.app', establishing exactly what the tool retrieves. It effectively distinguishes this from siblings get_world_briefing (data retrieval) and submit_correction (mutation) by positioning this as the documentation/introspection tool for understanding the system itself.
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 implies invocation context through phrases like 'best practices for grounding AI responses', suggesting when the tool provides value. However, it lacks explicit when-to-use instruction (e.g., 'Call this first when uncertain about tool capabilities') or explicit exclusion criteria contrasting it with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_world_briefingAInspect
Current 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: data freshness (3x daily), outdated training data warning, default token sizes, and that section/sections override format. No contradictions or omissions for a read-only fact-retrieval tool.
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 well-structured with a bold warning at the top, then categorized content areas. It is long (multiple paragraphs) but each section adds necessary detail. Could be slightly trimmed without losing clarity, but the structure earns a high score.
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 output schema, the description thoroughly explains what the tool returns through example sections (heads of state, wars, prices, etc.) and mentions defaults. For a simple tool with 3 optional parameters, this is comprehensive and leaves no ambiguity.
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?
Despite 100% schema coverage, the description adds significant value by explaining the token sizes for each format ('~14,000', '~8,000', '~1,500') and providing specific examples for sections (e.g., 'economy' for rates, 'ai_model_versions' for pricing). This goes beyond the enum names.
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, distinguishing it from the sibling 'get_help' which presumably offers documentation. It specifies the verb 'get' and resource 'world briefing' unambiguously.
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 explicitly instructs to call this tool before any output depending on current facts, listing detailed scenarios (questions, code, emails, etc.). It provides clear context on when to use, though does not explicitly state when not to use it, which is acceptable given its broad applicability.
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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