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mcp-knowledge

by io.github.getgapup

weather_climate_intel

Read-only

Physical climate intelligence for insurance underwriting, agritech, logistics, energy trading and ESG/climate risk disclosure. Three modes: (1) forecast — 14-day daily weather forecast with temperature, precipitation, wind and humidity; (2) historical — daily records and monthly aggregates for any date range since 1940, with anomaly detection (P90/P95 heat events, extreme precipitation days); (3) climate_risk — long-term physical risk scoring combining CMIP6 ensemble projections (2020-2050), altitude, FEMA flood zones (US) and historical baselines. Risk dimensions: flood, heat (days >35°C/year), drought (SPI), wildfire, sea-level. Overall score 0-100 (100 = severe). Location: city string or lat/lon coordinates. Sources: Open-Meteo (keyless, global, 1940→2050), Open-Elevation, FEMA NFHL (US), NOAA CDO (optional NOAA_API_KEY env var for US+global station data). SLA: ≤25s p95. Cache: 1h forecast / 24h historical / 7d climate_risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes'forecast' (14 days), 'historical' (date range since 1940), 'climate_risk' (long-term physical risk score)
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
date_toNoISO date YYYY-MM-DD — end of date range (required for historical/climate_risk)
metricsNoWeather metrics to include. Default: all metrics.
locationYesGeographic location. Provide either {city, country?} or {lat, lon}.
date_fromNoISO date YYYY-MM-DD — start of date range (required for historical/climate_risk)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
sourcesYes
forecastNo
locationYes
historicalNo
climate_riskNo
quality_scoreYes

TDQS

A4.7/5.0
Behavior5/5

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

The description adds significant context beyond annotations, including keyless external API usage, SLA (≤25s p95), cache durations, and the async option. It explains the data pipeline (Open-Meteo, Open-Elevation, FEMA, NOAA). No contradiction with annotations (readOnlyHint=true, openWorldHint=true).

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 comprehensive but somewhat verbose. It is well-structured with numbered modes and clear sections (sources, SLA, cache). Every sentence adds value, but given the complexity, the length is justified. Could be slightly more terse without losing clarity.

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?

The description covers all necessary aspects: usage modes, input parameters, data sources, performance expectations, caching behavior, and the existence of an output schema. For a tool with 6 parameters, nested objects, and three modes, this provides complete guidance for proper invocation.

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

Parameters5/5

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

All parameters have schema descriptions (100% coverage). The description adds meaning by explaining how the 'mode' parameter affects behavior, clarifying location options (city vs lat/lon), and specifying that metrics default to all. This enriches the schema's information.

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 it provides 'Physical climate intelligence' and enumerates three distinct modes: forecast, historical, climate_risk. Each mode is described with specific use cases, sources, and output characteristics. This specificity distinguishes it effectively from sibling 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 explicitly outlines when to use each mode (forecast 14-day, historical since 1940, climate_risk long-term). It provides context such as SLA, cache policies, and data sources. While it doesn't include explicit 'when not to use' statements, the detailed mode descriptions implicitly guide selection.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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