opendata_explain_choice
Explain the scoring breakdown for a provider search. Shows how each provider was ranked using token matching, fuzzy matching, semantic similarity, and metadata filters.
This record as markdown: /tools/io-github-derekslinz-meta-data-mcp/opendata-explain-choice.md
What opendata_explain_choice does on Meta Data Mcp
AI agents call opendata_explain_choice to retrieve information from Meta Data Mcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why opendata_explain_choice is rated Low
This tool queries and explains existing search results and ranking data. It performs no side effects—no data is created, modified, deleted, or executed. The action is purely informational: returning explanatory metadata about how providers were scored. This is a clear Read operation with low severity (non-sensitive retrieval of search scoring logic).
From the tool's definition Tool name 'opendata_explain_choice' and description 'Explain the scoring breakdown for a provider search. Shows how each provider was ranked...' indicates a retrieval and display of pre-computed scoring information with no data modification, deletion, or…
Attacks that exploit this kind of access
The rule that runs opendata_explain_choice safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Meta Data Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For opendata_explain_choice, this is the rule to start with:
opendata_explain_choice is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Meta Data Mcp, apply this rule, and every opendata_explain_choice call is checked against it from then on.
Questions about opendata_explain_choice
Explain the scoring breakdown for a provider search. Shows how each provider was ranked using token matching, fuzzy matching, semantic similarity, and metadata filters. It is categorised as a Read tool in the Meta Data Mcp MCP Server, which means it retrieves data without modifying state.
Register the Meta Data MCP server in PolicyLayer and add a rule for opendata_explain_choice: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Meta Data Mcp. Nothing to install.
opendata_explain_choice is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the opendata_explain_choice rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for opendata_explain_choice. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
opendata_explain_choice is provided by the Meta Data MCP server (pypi:meta-data-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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