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LedgerProof LAIN — Verified Evidence for Agents

fund_opportunity

Fund a discovered Opportunity into a real, authority-backed, proof-gated economic event. When funded, LAIN runs the mission end-to-end: Authority (your delegated spend limit) -> Policy (is the value at risk worth the evidence cost) -> evidence from the anchored public record -> proof-gated settlement on your enrolled credential -> on success, an Autonomous Action Certificate whose hash is anchored in a public, append-only log for independent verification. Requires an enrolled credential (your Authority Receipt). Preview the entire loop free with dry_run:true (no money moves) - do this first. Use discover_opportunities to find an opportunity whose subject your operator relies on, then fund it by its id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNotrue = free preview of the whole loop, no charge
risk_valueNodollars at stake if the decision is wrong - drives the policy SHOULD-gate
budget_centsNomax you will spend (defaults to your per-transaction authority)
opportunity_idYesthe Opportunity id from discover_opportunities, e.g. opp_us-cpsc:10351
current_confidenceNo0..1, your confidence now (default derived from the opportunity's gap)
required_confidenceNo0..1, your policy threshold (default 0.99)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the multi-step process, prerequisites (enrolled credential), the nature of the economic event, and the dry-run preview. It does not explicitly state irreversibility or failure modes, but the high-level side effects are well covered.

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?

The description is dense but structured, front-loaded with the primary verb, and each sentence contributes a distinct piece of information (process, prerequisite, preview, workflow). It is perhaps longer than minimal, but all content is relevant and no filler.

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?

Given the lack of output schema, the description still indicates the output (Autonomous Action Certificate with anchored hash). It covers prerequisites, safety preview, and the discovery→fund workflow, making it comprehensive for a tool with 6 parameters and no output schema.

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?

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the role of dry_run ('no money moves', 'do this first'), the meaning of risk_value ('drives the policy SHOULD-gate'), and the reference to opportunity_id ('by its id'). This goes beyond the schema's field descriptions.

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?

The description clearly states the action ('Fund a discovered Opportunity') with a specific resource and purpose ('real, authority-backed, proof-gated economic event'). It distinguishes itself from siblings by explicitly contrasting with discover_opportunities and outlining the end-to-end flow.

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 workflow guidance: 'Use discover_opportunities to find... then fund it by its id' and 'Preview the entire loop free with dry_run:true - do this first.' It lacks explicit when-not-to-use statements, but the contextual workflow 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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct operation—discovery, funding, job polling, receipt minting, capability purchase, confidence resolution, graph reading, and verification. No two tools overlap in purpose, and the detailed descriptions make selection unambiguous.

Naming Consistency4/5

Most tools follow a verb_noun pattern (discover_*, fund_opportunity, get_job, get_sample_receipt, request_capability, resolve_confidence), but subject_evidence_graph is a noun phrase and verify is a bare verb, creating minor inconsistency.

Tool Count5/5

With 9 tools, the server is well-scoped, covering discovery, funding, execution, and verification without redundancy or bloat.

Completeness5/5

The tool surface covers the full evidence lifecycle: discover opportunities, fund them, poll jobs, mint receipts, request capabilities, resolve confidence gaps, read evidence graphs, and verify receipts. No obvious operational gaps.

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