Skip to main content
Glama

gapup-mcp

by io.gapup.mcp

climate_scenario_rcp

Read-only

Projections climatiques long terme par scénario IPCC (RCP AR5 + SSP AR6) pour toute localisation. Scénarios : RCP_4_5, RCP_8_5 (AR5), SSP1_2_6, SSP2_4_5, SSP3_7_0, SSP5_8_5 (AR6), ou 'all' (compare tous). Horizons : 2030–2100. Métriques : température (delta vs baseline 1990-2010, jours >35°C, nuits chaudes), précipitations (delta%, événements extrêmes, sécheresses), hausse du niveau de la mer (cm vs 2000), événements extrêmes (ouragans, inondations P100, sécheresses), indice incendie. Sorties : comparaison multi-scénarios, probabilité IPCC, signaux d'impact business par secteur. Sources : Open-Meteo CMIP6 (keyless), IPCC AR6 Atlas lookup, NOAA SLR projections. Usages : TCFD/CSRD physical risk, due diligence actifs long terme, assurance catastrophe, planification infrastructure. Cache 7j. SLA ≤20s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
metricsNoMétriques à inclure. Défaut : toutes.
locationYesLocalisation : {city, country?} ou {lat, lon}
scenarioYesScénario IPCC. 'all' génère une comparaison multi-scénarios.
horizon_yearYesAnnée horizon de la projection (2030–2100)
compare_baselineNoComparer vs baseline 1990-2010 (défaut true)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
locationYes
scenarioYes
projectionsYes
horizon_yearYes
quality_scoreYes
baseline_periodNo
ipcc_likelihood_labelYes
business_impact_signalsYes
multi_scenario_comparisonNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations show readOnlyHint=true, destructiveHint=false, idempotentHint=false. The description adds behavioral details: caching (7 days), SLA (≤20s), and async behavior. This is consistent and provides useful operational context beyond annotations.

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 a single paragraph that front-loads the main purpose and uses efficient, information-dense sentences. Could benefit from bullet points for readability, but every sentence adds value without redundancy.

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 the complexity (6 parameters, nested objects, enums, output schema), the description covers all necessary aspects: scenarios, metrics, horizon, location, use cases, data sources, caching, and SLA. The output schema exists so return value details are not required.

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 description adds value by explaining parameter context: what 'all' scenario does, metric descriptions (temperature delta vs baseline, extreme events), and location flexibility (city/country or lat/lon). This enriches understanding beyond the schema.

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: long-term climate projections under IPCC scenarios (RCP and SSP) for any location. It lists specific scenarios, metrics, horizons, and output types, distinguishing it from sibling tools like weather_climate_intel by focusing on IPCC-based projections for TCFD/CSRD risk assessment.

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 explicit use cases (TCFD/CSRD physical risk, due diligence, insurance, infrastructure planning) and mentions multi-scenario comparison. However, it does not explicitly exclude or compare to other tools like weather_climate_intel, leaving room for ambiguity about alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All gapup-mcp toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.5/5.0
Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources