SeaWeb

get_camera_visibility

Read-onlyIdempotent

Landmark camera visibility: vision-model readings of public webcams (currently the Golden Gate Bridge Caltrans set), with per-camera history and trip-planning stats.

Each camera row carries the latest reading (`vision`: visibility_percentage
0-100, environmental_conditions, obstruction_flags, operational_action
Proceed|Delay|Reroute), a 24h `history` timeline, and `hourly` clear-window
averages once >= 2 days of readings exist ("usually clearest 11:00-16:00").
The top-level `verdict` is the best reading no older than 2 hours — stale
rows still appear on their camera but never speak for the group.

Honesty labels, worth stating plainly: every reading is a vision model
looking at ONE still frame from a fixed roadway camera near the landmark —
not an NWS station, not a forecast. A camera serving a placeholder or an
unreadable frame is recorded as "indeterminate" and excluded from stats
and verdicts rather than shipped as a number. `verdict: null` means no
camera produced a fresh readable frame, not that conditions are clear.
An empty `cameras` list means the ingest worker has not run against this
gateway. Readings update every ~30 minutes (SEAWEB_CAMERA_VLM_INTERVAL_MIN).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds substantial behavioral context: readings are from a single still frame, not continuous video; staleness handling (verdict null if no fresh frame); placeholder/unreadable frames marked as indeterminate; update cadence (~30 min); and caveats about empty camera lists. This goes well beyond annotations, making the tool's limitations and data quality explicit.

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 with concise paragraphs and front-loaded key information (purpose and scope). The section on honesty labels is valuable and not redundant. It is slightly long but every sentence adds value, such as explaining confident interpretation of null verdicts.

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

Completeness4/5

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

Given the tool's moderate complexity (one param, no output schema, rich annotations), the description covers key details: data freshness, staleness, data provenance, and failure modes. However, it lacks explicit note on how to interpret or use limit, and the output structure is partially explained but not fully (e.g., exact fields in history). This is near-complete but with minor gaps.

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

Parameters3/5

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

The schema has a single optional parameter (limit) with default 25 and no description coverage. The description does not mention how limit affects results (e.g., number of cameras returned), which is a gap. However, with only one simple parameter, the baseline is 3; the description's lack of detail doesn't severely penalize.

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 description clearly states the tool reads landmark camera visibility from public webcams, identifies the specific camera set (Golden Gate Bridge Caltrans), and distinguishes it from weather stations or forecasts. It specifies the resource (vision-model readings of webcams) and the action (getting visibility), making it distinct from sibling tools like weather or search tools.

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 provides clear context on when to use this tool (for camera-based visibility readings, not forecasts), but does not explicitly state when not to use it or name alternatives. It mentions that it's not an NWS station, but does not point to alternative tools like travel_health or search_web. This is a minor gap.

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

B3.4/5.0

Disambiguation3/5

There is notable overlap among search, search_web, search_restaurants, and search_salons, as well as between filter_restaurants/filter_salons and search with constraints. However, descriptions clarify the intended vertical or corpus, and entity getters are distinct. The overlap is manageable but could cause misselection.

Naming Consistency4/5

Names mostly follow a get_/list_/search_/register_/delete_/submit_/vote_ pattern in snake_case. Minor deviations like 'recall', 'remember', 'research', and 'travel_health' are less predictable but still readable. Overall consistent and clear.

Tool Count2/5

38 tools is on the heavy side for a single MCP server, exceeding the typical well-scoped range. While the server covers multiple subdomains (search, travel disruptions, memory, feedback, research), the sheer number may overwhelm agents and suggests potential consolidation.

Completeness4/5

The tool surface covers core workflows: search and entity retrieval for restaurants/salons, disruption monitoring with standing queries and webhooks (register/list/delete), research submission/polling, and memory/feedback mechanisms. Minor gaps exist (e.g., no cancel for research jobs, no explicit entity list endpoint), but these are workable and do not break typical agent tasks.

Resources

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