utsunomiya-mcp-server
Provides an MCP server that exposes Utsunomiya City open data via natural-language queries and MCP tools (list datasets, get dataset schemas, query dataset contents, and analyze datasets with caching).
Score Breakdown
⚙ Agent Friendliness
🔒 Security
No auth model is documented; if the MCP server is publicly reachable, it may be vulnerable to unrestricted access (not confirmed). TLS enforcement and server network exposure are not described in the materials. Dependencies include common libraries (pandas/requests/pydantic); no vulnerability or pinning information is provided to assess CVE risk.
⚡ Reliability
Best When
You want an agent to quickly explore and query a known public dataset catalog through MCP tools using a local command-line server process.
Avoid When
You cannot tolerate unknown error behavior, undefined rate limiting, or lack of demonstrated auth/security controls for tool access.
Use Cases
- • Agent-assisted discovery of available open datasets
- • Schema discovery for downstream analytics or data mapping
- • Filtered querying of dataset records
- • Basic descriptive statistics / analysis over dataset values
- • Hands-off integration in MCP-capable clients (e.g., Claude Desktop)
Not For
- • Use as a general-purpose data platform for non-Utsunomiya sources
- • Production systems that require strong guarantees around data quality, auditing, or contractual SLAs (not evidenced in provided materials)
- • Environments needing strict authentication/authorization controls for the API tools (not evidenced)
Interface
Authentication
No authentication mechanism is described in the provided README or manifest. MCP server likely runs locally and accesses public open data without user auth, but this is not explicitly guaranteed.
Pricing
No pricing information provided.
Agent Metadata
Known Gotchas
- ⚠ No documented pagination/limits strategy is provided in the README snippet.
- ⚠ No explicit retry/idempotency guidance is provided for tool calls.
- ⚠ No documented error schema or failure modes are described; agent behavior under partial failures is uncertain.
Alternatives
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Scores are editorial opinions as of 2026-04-04.