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gapup-mcp

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?

Despite annotations already declaring readOnlyHint and destructiveHint false, the description goes far beyond them with rich behavioral details: data sources (Open-Meteo, Open-Elevation, FEMA, NOAA), SLA (≤25s p95), cache durations (1h/24h/7d), optional API key, and async behavior for slow requests. This provides exceptional transparency about how the tool operates.

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

Conciseness5/5

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

The description is a single, dense paragraph that front-loads the main purpose and then efficiently covers modes, risk dimensions, location inputs, data sources, SLA, and caching. Every sentence adds unique value, with no filler or repetition. The structure is logical, progressing from high-level overview to specifics.

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 is exceptionally complete for a complex tool with three modes and nested parameters. It covers purpose, modes, risk dimensions, location formats, data sources, performance expectations, caching, and optional configuration. Given that an output schema exists, the description appropriately avoids repeating return-value details, yet still provides enough contextual depth for an agent to invoke the correct mode and parameters.

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?

Schema coverage is 100%, so the schema already documents all parameters. The description adds meaningful context above that, such as explaining that 'historical' mode includes anomaly detection and date ranges since 1940, and that 'climate_risk' aggregates CMIP6 projections and risk scoring dimensions (flood, heat, drought, etc.). This deepens the agent's understanding of the 'mode' parameter's semantics and the tool's domain expertise.

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's purpose with specific verbs and resources: 'Physical climate intelligence for insurance underwriting...' and enumerates three distinct modes (forecast, historical, climate_risk). It is highly specific and differentiates itself from a wide range of siblings by focusing on weather/climate data with detailed mode definitions.

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 the tool via explicit use cases ('insurance underwriting, agritech, logistics, energy trading') and explains the three modes, which helps the agent choose the appropriate configuration. However, it does not explicitly mention when not to use it or name alternative tools, so a perfect 5 is not warranted.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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