ckg-agentforce

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evaluate_trust_chain

Evaluate cryptographic trust chain and ROI of a knowledge graph to decide autonomous purchase. Returns JSON with source hashes, graph hash, signer key, benchmark proof, SCITT status, and payment recommendation.

Instructions

Return a machine-readable JSON trust chain + ROI analysis + payment recommendation.

Designed for autonomous agent consumption. The agent reads the JSON, evaluates the ROI gate, selects the cheapest positive-ROI payment option, and purchases autonomously if budget is pre-authorized.

Trust chain closes the audit gap that RAG cannot: source_hashes → SHA-256 of source docs (what was known at extraction time) graph_hash → SHA-256 of the installed graph file (what is declared now) signer_key_id → Ed25519 public key fingerprint (who is attesting) benchmark_receipt → BoundaryAttest-signed F1 claim (cryptographic performance proof) scitt_status → SCITT ledger submission status (public audit anchor)

ROI gate: if total_value_per_call_usd > payment_option.price_per_call_usd → autonomous_pay = true.

Returns: JSON string (schema: ckg-trust-chain-v1).

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.9.1Jul 29, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. Explains return format, trust chain components, and ROI logic. Lacks details on side effects or state changes, but tool is read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with main purpose followed by bullet details. Slightly verbose in explaining agent behavior, but overall concise and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, output schema exists, and description explains return content and logic comprehensively. No gaps remain for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has zero parameters; schema coverage is 100%. Baseline of 4 applies as description adds no parameter info beyond 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?

Clearly states the tool returns a machine-readable JSON trust chain, ROI analysis, and payment recommendation. Distinct from sibling tools like export_benchmark_receipt which only exports, or query_ckg which lacks ROI analysis.

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?

Provides explicit guidance for autonomous agent consumption, including how to interpret ROI and proceed with payment. Does not explicitly mention alternatives or exclusions, but context is clear.

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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