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Server Details

Czech distressed real estate — anonymized district aggregates (k≥5). Free tier.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
martinhavel/cz-agents-mcp
GitHub Stars
2
Server Listing
cz-agents-mcp

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MCP server

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Tool DescriptionsA

Average 4.2/5 across 1 of 1 tools scored.

Server CoherenceB
Disambiguation5/5

Only one tool exists, so there is no possibility of ambiguity or confusion between tools.

Naming Consistency5/5

With a single tool, the naming pattern is inherently consistent; 'get_district_aggregate' follows a clear verb_noun structure.

Tool Count2/5

The server covers a broad 'realestate' domain but exposes only one tool, which is too few for the apparent scope and results in a thin tool surface.

Completeness1/5

The single tool provides only aggregate district statistics for Czech real estate. All other potential real estate operations (listings, transactions, etc.) are missing, making the surface severely incomplete.

Available Tools

1 tool
get_district_aggregateA
Read-only
Inspect

Aggregate distress real estate statistics for a Czech okres (district). Returns counts by category (insolvency / auction) and average market data. Low-volume districts are marked with low_activity. Free tier — no PII exposed.

ParametersJSON Schema
NameRequiredDescriptionDefault
okresYesCzech okres name (e.g. "Praha", "Brno-město", "Beroun"). Case-sensitive.
window_daysNoLookback window in days. Default 90.
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the description's main behavioral contribution is stating 'Free tier — no PII exposed' and noting the low_activity flag. While this adds value, it does not cover all behavioral aspects (e.g., no rate limits or auth requirements). The description is consistent with annotations and provides modest additional transparency.

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 three sentences long, with the main purpose in the first sentence. Every sentence adds unique information: aggregate function, return contents, and noteworthy constraints (low_activity, free tier, no PII). No fluff or repetition.

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 simplicity (2 parameters, no output schema), the description adequately covers what the tool does and the nature of its output. It mentions counts by category, average market data, and the low_activity flag. However, it could be improved by describing the exact fields returned, but the current level is sufficient for an AI agent to understand the tool's purpose.

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?

Schema description coverage is 100% with detailed parameter descriptions (e.g., case-sensitive, examples, default value for window_days). The tool description adds minimal parameter-specific information beyond the schema, only mentioning 'Low-volume districts are marked with low_activity' which relates to output, not input params. Baseline 3 is appropriate as the schema already carries the burden.

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?

Description clearly states the verb 'Aggregate' and the resource 'distress real estate statistics for a Czech okres (district)'. It specifies what is returned (counts by category, average market data, low_activity flag) and includes additional context (free tier, no PII). With no sibling tools, differentiation is not needed, and the purpose is unambiguous.

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 the tool should be used for district-level aggregate statistics in the Czech Republic, but does not explicitly state when to use it or provide exclusions. Since there are no sibling tools, lack of explicit alternatives is acceptable. The description is clear enough for an AI agent to identify the correct context.

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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