gapup-mcp

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

Name	Required	Description
`to`	No	routing only: destination address or 'lat,lon'
`from`	No	routing only: origin address or 'lat,lon'
`mode`	Yes	'geocode_batch': address -> lat/lon. 'routing': route + ETA. 'port_congestion': UN/LOCODE port state. 'ship_tracking': vessel by MMSI
`async`	No	If 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.
`query`	Yes	Primary input: address for geocode/routing, UN/LOCODE (e.g. NLRTM) for port_congestion, 9-digit MMSI for ship_tracking
`addresses`	No	geocode_batch only: up to 50 addresses (overrides query if provided)
`mode_transport`	No	routing only: transport mode. Default: driving

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`status`	Yes
`routing`	No
`sources`	Yes
`geocode_batch`	No
`quality_score`	Yes
`ship_tracking`	No
`port_congestion`	No

TDQS

A4.7/5.0

Behavior5/5

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

Annotations declare readOnlyHint=true and destructiveHint=false, which the description aligns with. The description adds substantial behavioral context beyond annotations: rate limits (1 req/s), SLA (≤25s p95), caching durations (24h geocoding, 1h routing, 30min port, 5min ship), fallback mechanisms (Open-Meteo), and best-effort behavior for ship tracking without API key. No contradictions.

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 dense but well-organized as a single paragraph listing modes numerically. Every sentence adds value, though bullet points or subsection headers could improve readability. It is appropriately sized for the tool's complexity.

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 (4 modes), complete annotations, and full schema coverage, the description covers all necessary aspects: input formats, output details (lat/lon, route parameters, congestion metrics, AIS data), performance characteristics, cache durations, and fallback behavior. It is comprehensive and leaves no significant gaps.

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?

Schema coverage is 100%, but the description adds significant meaning: it explains the role of each parameter in context (e.g., 'addresses overrides query for geocode_batch'), provides examples (NLRTM, lat,lon), and clarifies constraints (max 50 addresses). This goes beyond the schema's property 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: 'Geospatial logistics intelligence for supply chain, maritime and transport agents.' It explicitly lists four modes (geocode_batch, routing, port_congestion, ship_tracking) with specific verbs and resources, distinguishing it from siblings which are unrelated.

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 when-to-use guidance for each mode (e.g., 'resolve up to 50 addresses to lat/lon', 'route between two points', 'congestion status for any UN/LOCODE port', 'AIS position for a vessel by MMSI'). It also specifies prerequisites (no API key needed for most, optional for ship tracking) and constraints (rate limits, SLA, cache durations). However, it does not explicitly state when not to use this tool or compare to alternative tools, leaving room for improvement.

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

C2.5/5.0

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.

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