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Glama

realestate

Server Details

Czech distress real-estate — anonymized district aggregates (k≥5). Free tier only.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
martinhavel/cz-agents-mcp
GitHub Stars
0
Server Listing
cz-agents-mcp

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Tool DescriptionsA

Average 4.4/5 across 1 of 1 tools scored.

Server CoherenceA
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion between tools. Its purpose is clearly distinct by default.

Naming Consistency5/5

The single tool name 'get_district_aggregate' follows a consistent verb_noun pattern, which is trivially consistent across the set.

Tool Count3/5

With only one tool, the server feels thin for a domain like real estate. While the tool itself is well-scoped, a single tool is borderline for a complete server.

Completeness3/5

The tool provides aggregate statistics but lacks other likely operations such as listing districts, individual property data, or filtering. Notable gaps exist given the server's broad name.

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. Counts under 5 are suppressed (k-anonymity gate) to prevent identifying specific debtors in low-activity districts. 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.
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses a key behavioral trait: counts under 5 are suppressed for k-anonymity. This is critical for understanding tool output and is not implied by annotations.

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?

Three sentences: purpose, output summary, privacy note. Every sentence adds value, front-loaded, no fluff.

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?

No output schema, but description adequately states return types (counts, average market data) and the suppression rule. Could benefit from noting the output format (e.g., JSON structure) but sufficient for an aggregate tool.

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 coverage is 100%, so the description adds no new parameter details. The description mentions 'Czech okres name' and 'lookback window' but these are already in 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?

Description clearly states it aggregates distress real estate statistics for a Czech okres, with specific output like counts by category and average market data. No sibling tools exist, so no differentiation needed.

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?

Description provides clear context for use (statistical aggregates for a district) and notes the free tier and no PII exposure, but does not explicitly state when not to use it or mention alternatives (none available).

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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