Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

building_enrich

Enrich a location with European building intelligence: roof surfaces (m²), parking areas, solar-obligation status under French loi APER and loi Climat-Résilience, existing solar installations. Covers 628,000 scanned roofs and 91,800 parkings across 6 EU countries (FR, DE, IT, ES, BE, NL). Deterministic database lookup — no LLM, no generation, sub-second.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude (WGS84)
lngYesLongitude (WGS84)
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.
radiusMNoSearch radius in metres (default 150, max 500)
minAreaM2NoOnly return roofs/parkings at least this large, in m²

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
roofsNo
summaryNo
parkingsNo
solarInstalledNo

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: 'Deterministic database lookup — no LLM, no generation, sub-second.' This reassures the agent about predictability and speed. It does not explicitly state read-only behavior, but 'database lookup' implies it. Additional details like coverage counts further clarify the nature of the operation.

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 two sentences, front-loaded with the primary verb and resource. It packs essential info (data types, coverage, performance) without unnecessary words. Every element earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multi-country, multiple data types, legal references), the description is complete enough to set expectations. It covers scope, coverage, and performance. It does not detail edge cases or coverage limitations, but an output schema exists, and the schema itself documents async behavior, so the description is sufficient for initial selection.

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

Parameters3/5

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

The schema has 100% param coverage, so the description need not repeat parameter details. It does add context that the location parameters relate to buildings and parking, and mentions units (m²) which align with minAreaM2. However, this does not go beyond what the schema already communicates, so it earns the baseline score.

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 function: 'Enrich a location with European building intelligence' and lists specific data types (roof surfaces, parking areas, solar-obligation status, existing solar installations). It also differentiates from siblings by specifying the geographic coverage and deterministic nature.

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 implies when to use the tool: when needing building-related data for a European location. It provides context about the database coverage and speed ('sub-second'), which helps the agent judge suitability. However, it does not explicitly mention alternatives or exclusion conditions, so it falls short of full guidance.

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

All mcp-knowledge 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

C2.8/5.0
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.

Resources