tide_data
NOAA tide predictions and observed water levels for coastal stations.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | YYYYMMDD | |
| station | No | NOAA station ID |
tollbooth
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.6/5.0
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description provides no behavioral details beyond the obvious read-only nature. With empty annotations, the description carries full burden, but it doesn't disclose rate limits, data latency, or response structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence description is concise but lacks structure. It is not verbose, but could be improved with minimal expansion.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite low complexity (2 parameters, no nested objects), the description omits output format or example data. Without output schema, an agent cannot infer what the tool returns (e.g., a list of tides, single value).
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?
Input schema covers both parameters with clear descriptions (YYYYMMDD for date, NOAA station ID for station). The description adds no additional meaning, so baseline 3 is appropriate given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description states it provides 'NOAA tide predictions and observed water levels for coastal stations,' clearly indicating data source and type. This distinguishes it from many sibling tools that focus on weather or environmental data, though not from 'water_levels' specifically.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'water_levels' or other coastal data tools. The context is implied but not explicit.
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
C2.2/5.0
Disambiguation3/5
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming Consistency2/5
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
Tool Count1/5
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Completeness2/5
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.