Fuzzy-Match Calibration Scorer — OpenChainGraph compute node (model_governance). Runs deterministically in-browser; zero PII, zero egress. Exports an AP2 artifact with execution_hash for chain provenance. Consumes upstream artifacts from: art-90-sanctions-screening-fit-diagnostic. Output feeds: a...
AI agents call score_fuzzy_match_calibration to retrieve information from Ainumbers Mcp Apps without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
compute | string | — | Compute mode (v0.4 Compute Binding). "auto" (default) = server for gpu:false nodes with registered kernels; "server" = force server-side; "browser" = always ret |
parent_hashes | array | — | execution_hash values from upstream ChainGraph AP2 artifacts to chain from (sets chain.parent_hashes in the export). |
parent_tool_ids | array | — | tool_id values matching parent_hashes, in the same order. |
policy_parameters | object | — | Input parameters for this tool's decision function. For gpu:false nodes with a registered kernel, these are computed server-side when compute is "auto" or "serv |
Parameters from the server's own tool schema.
This is a read-only analytical tool that consumes upstream artifacts, performs deterministic scoring computations, and exports provenance artifacts. There are no side effects (reversible or irreversible), no command execution, and no financial operations. The 'zero PII, zero egress' statement and browser-based, deterministic nature confirm it is a data query/analysis tool with no capability to modify external state.
From the tool's definition 'Runs deterministically in-browser; zero PII, zero egress. Exports an AP2 artifact with execution_hash for chain provenance.' The tool scores/calibrates fuzzy-match results and generates audit artifacts.
Attacks that exploit this kind of access
Fuzzy-Match Calibration Scorer — OpenChainGraph compute node (model_governance). Runs deterministically in-browser; zero PII, zero egress. Exports an AP2 artifact with execution_hash for chain provenance. Consumes upstream artifacts from: art-90-sanctions-screening-fit-diagnostic. Output feeds: art-97-sanctions-screening-quality-scorer, cry-05-agent-action-audit-trail-aggregator. Open at: https://ainumbers.co/chaingraph/art-93-fuzzy-match-calibration-scorer.html. It is categorised as a Read tool in the Ainumbers Mcp Apps MCP Server, which means it retrieves data without modifying state.
score_fuzzy_match_calibration accepts 4 parameters: compute, parent_hashes, parent_tool_ids, policy_parameters. The full parameter table on this page comes from the server's own tool schema.
Register the Ainumbers Mcp Apps MCP server in PolicyLayer and add a rule for score_fuzzy_match_calibration: 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 Ainumbers Mcp Apps. Nothing to install.
score_fuzzy_match_calibration 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 score_fuzzy_match_calibration 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 score_fuzzy_match_calibration. 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.
score_fuzzy_match_calibration is provided by the Ainumbers Mcp Apps MCP server (postoaklabs/ainumbers-mcp-apps). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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