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mcp-knowledge

by io.github.getgapup

geo_logistics_intel

Read-only

Geospatial logistics intelligence for supply chain, maritime and transport agents. Four modes: (1) geocode_batch — resolve up to 50 addresses to lat/lon with confidence scores (OSM Nominatim + Open-Meteo fallback, 1 req/s rate-limit respected); (2) routing — road/cycling/walking route with distance_km, duration_seconds and ETA ISO timestamp between two addresses or lat/lon points (OSRM public, keyless, global); (3) port_congestion — congestion status for any UN/LOCODE port (e.g. NLRTM, SGSIN, CNSHA) with waiting vessel count, severity (low/medium/high/extreme) and average wait hours; (4) ship_tracking — AIS position, speed, course, destination and ETA for a vessel by its 9-digit MMSI. No API key required for geocode/routing/port. Optional env: AIS_STREAM_API_KEY for live ship data (otherwise MarineTraffic scrape best-effort). SLA: <=25s p95. Cache: 24h geocoding / 1h routing / 30min port / 5min ship. Quality score 0-100. Status: final/partial/failed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNorouting only: destination address or 'lat,lon'
fromNorouting only: origin address or 'lat,lon'
modeYes'geocode_batch': address -> lat/lon. 'routing': route + ETA. 'port_congestion': UN/LOCODE port state. 'ship_tracking': vessel by MMSI
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.
queryYesPrimary input: address for geocode/routing, UN/LOCODE (e.g. NLRTM) for port_congestion, 9-digit MMSI for ship_tracking
addressesNogeocode_batch only: up to 50 addresses (overrides query if provided)
mode_transportNorouting only: transport mode. Default: driving

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
routingNo
sourcesYes
geocode_batchNo
quality_scoreYes
ship_trackingNo
port_congestionNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and nondestructive hints. The description adds behavioral details beyond annotations: rate limit of 1 req/s, SLA of ≤25s p95, cache durations (24h geocoding, 1h routing, 30min port, 5min ship), and fallback mechanisms (Open-Meteo, best-effort scrape). No contradiction with 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 well-structured with clear enumeration of modes and additional details (SLA, cache, quality). It is slightly dense but each sentence contributes necessary information. Concise for the breadth of functionality covered.

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 tool's complexity and the presence of an output schema, the description covers all critical aspects: modes, inputs, limitations (rate limit, cache), fallback behavior, API key requirements, and performance metrics. No gaps identified.

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 baseline is 3. The description adds value by explaining the role of each mode in relation to parameters (e.g., 'address for geocode/routing, UN/LOCODE for port_congestion, 9-digit MMSI for ship_tracking'), and clarifies the 'addresses' parameter for batch geocoding. This goes beyond the schema's basic descriptions.

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 as geospatial logistics intelligence with four distinct modes (geocode_batch, routing, port_congestion, ship_tracking), each with specific functionality. It distinguishes the tool from siblings, which are mostly unrelated, ensuring the agent understands the tool's unique value.

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 usage context for each mode, including input types and constraints (e.g., up to 50 addresses for geocode, UN/LOCODE for port, MMSI for ship). It also mentions when API keys are needed, but does not explicitly exclude alternatives among siblings, which are largely non-overlapping.

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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