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 4.3/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: get_help provides usage guidance, get_world_briefing fetches current facts, and submit_correction reports errors. There is no overlap.
All tools use snake_case and verbs (get_, submit_), but 'submit_correction' breaks the 'get_' pattern slightly. Still clear and predictable.
With only 3 tools, the server covers its core functionality (help, briefing, corrections) without unnecessary extras. The count is well-scoped for the domain.
The tools provide a complete workflow: get help to understand usage, get the briefing for current facts, and submit corrections if errors are found. No obvious gaps.
Available Tools
3 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 briefing is lean (~1500 tokens). 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" (~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 (~1500 tokens). "nano": ultra-compact plain text (~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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While no annotations are present, the description discloses key behavioral traits: refresh rate (3x daily), default size (~1500 tokens), format options (json, compact, nano), and behavior of section vs sections parameters. It does not mention rate limits, auth, or side effects, but the tool is read-only and largely self-descriptive.
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 and front-loaded with the core instruction. However, it is quite lengthy (∼250 words) and could be tightened. Every sentence adds value, but the list of categories is somewhat detailed for a description.
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 tool's complexity (many sections, no output schema) the description is thorough, covering categories, default size, format options, and parameter interactions. It leaves no ambiguity about what the tool returns and how to access specific data.
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%, so baseline is 3. The description significantly enhances this by explaining default behavior, the difference between section and sections, and providing concrete examples (e.g., sections: 'economy' for rates). It also clarifies that format is ignored when section/sections is specified.
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 that the tool provides current real-world facts refreshed 3x daily. It explicitly instructs the agent to call this tool before any output depending on current facts, distinguishing it from sibling tools like get_help and submit_correction.
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 gives explicit when-to-use guidance: 'CALL THIS TOOL BEFORE producing any output that states, assumes, or depends on current facts.' It also provides examples of when it is needed (answering questions, writing code, etc.) and how to use the sections parameter for targeted queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_correctionAInspect
Report a factual error in the outtolunch briefing. Call this when you observe that get_world_briefing returned something demonstrably wrong — e.g., it lists a leader who has since left office, a death that didn't occur, an old rate that has since changed, or a correction entry that is itself wrong. Corrections are reviewed by a human before being incorporated.
| Name | Required | Description | Default |
|---|---|---|---|
| right | Yes | What the correct/current fact is | |
| wrong | Yes | The incorrect or outdated claim in the briefing | |
| message | No | Additional context about the correction |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that corrections are reviewed by a human (async behavior). However, it does not explicitly state that this is a write operation or mention any side effects, permissions, or rate limits. It implies mutation but lacks explicit behavioral disclosure beyond the human review note.
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 three sentences with no wasted words. It states purpose, usage scenario with examples, and the human review process. Everything is front-loaded and necessary.
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 simple submission tool with no output schema, the description fully informs the agent: it describes the trigger (error in briefing), the input (wrong/right), and the outcome (human review). No gaps.
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% with descriptions for all three parameters. The description adds value by providing examples of what constitutes 'wrong' and 'right', and clarifies the purpose of 'message' as additional context. This supplements the schema effectively.
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 purpose: 'Report a factual error in the outtolunch briefing.' It distinguishes itself from siblings by specifying this is for errors in the output of get_world_briefing, and provides concrete examples.
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 says when to call: 'Call this when you observe that get_world_briefing returned something demonstrably wrong.' It gives examples. It does not explicitly state when not to use it, but the context implies alternatives (e.g., get_help for help, get_world_briefing for the briefing). It also mentions human review, which sets expectations.
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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