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Immersive Commons

by com.immersivecommons

Get Frontier Tower SF local weather + time

ic_context_get
Read-only

Current weather + local time for IC's home (Frontier Tower SF). Source: Open-Meteo, cached ~10min. Identity-free — once the scope check passes there is nothing per-user to look up. Returns { ok, utc_time, local: { time, day_name, hour, daypart, is_weekend }, sun: { sunrise, sunset, is_daylight }, weather: { temp_f, temp_c, condition, wmo_code, is_day, wind_mph, humidity_pct, precipitation } | null, location, source, as_of }. Args: none. Required scope: context:read.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses important behavioral traits beyond the annotations: data source (Open-Meteo), caching behavior (~10min), identity-free nature, and the full return structure with all fields. Annotations already provide readOnlyHint and openWorldHint, but the description adds valuable context about how the data is sourced and what the agent can expect in the response.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a brief purpose statement, followed by source/cache details, identity note, return shape, and scope requirement. While it is somewhat verbose (listing the entire return schema), every sentence provides valuable information. It maintains a logical flow and front-loads the main purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description fully specifies the return value structure, source, caching, and required scope. It covers all relevant aspects for an AI agent to understand what the tool does and what it returns. For a simple read-only context tool, it is completely adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, and the description explicitly notes 'Args: none.' With 0 parameters, the baseline is 4, and the description appropriately clarifies that no arguments are needed, eliminating any ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The title 'Get Frontier Tower SF local weather + time' and description clearly state the specific resource and action. The verb 'Get' combined with the precise location and data types (weather, time) makes the purpose unambiguous. It distinguishes itself from sibling tools which address different domains (events, directories, etc.).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states 'Args: none' and 'Required scope: context:read', clearly indicating when to invoke it (no parameters needed) and the authorization prerequisite. It also explains that it is identity-free, implying it is suitable for general context queries. However, it does not explicitly mention when not to use it or alternatives among siblings, though the simplicity makes that less critical.

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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TDQS

A3.7/5.0
Disambiguation4/5

Most tools are clearly scoped to distinct actions (e.g., ic_hack_apply vs. ic_hack_register, ic_rooms_create vs. ic_rooms_join). A few pairs could confuse an agent: floor10_submit_highlight vs. floorcast_push both submit HighlightStories but to different queues, and ic_directory_search / ic_agent_directory_lookup / ic_admin_list_members overlap in searching members. Overall, the long descriptions help disambiguate, but the volume requires careful reading.

Naming Consistency3/5

The dominant pattern is ic_<domain>_<verb>_<object> (e.g., ic_admin_list_pending_events, ic_headsets_checkout), but there are notable deviations: floor10_* and floorcast_* prefixes break the ic_ convention, and a few tools use noun-style names (ic_health, ic_capabilities, ic_donations_total). Verb placement also varies (get_* vs *_get, e.g., ic_get_my_membership vs. ic_membership_set_profile). Still, most names are readable and predictable.

Tool Count1/5

175 tools is an extreme count for a single MCP server, far beyond the 50+ threshold that indicates an unwieldy surface. While the platform covers many domains (events, files, hackathon, headsets, prints, rooms, etc.), bundling everything into one server makes discovery and selection difficult. This would be better split into several narrowly-scoped servers.

Completeness4/5

The tool set covers nearly every lifecycle for each domain: CRUD for files/folders, full hackathon admissions and judging, headset lending with waivers and incidents, print farm submission and handoffs, and room coordination. Minor gaps exist: no delete for files/folders, no cancel for events, and some actions (like revoking a Z.ai key or tearing down a room) are explicitly left to human console use. Overall, the surface is remarkably comprehensive for the stated scope.

Resources