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Glama

LedgerProof LAIN — Verified Evidence for Agents

by io.ledgerproofhq

discover_opportunities

FREE: machine-generated demand. LAIN continuously scans the anchored public record (FDA drug recalls, CPSC product recalls, and CVE/NVD revisions) for events that could invalidate warranted confidence in a subject, and mints each as an open Opportunity - an unresolved decision nobody has funded yet. Returns ranked opportunities with value estimates, confidence-gap estimates, and suggested prices (which fall when prior anchored evidence for that subject can be reused). If an opportunity's subject matters to your operator (a supplier, a product in inventory, a dependency), fund it with fund_opportunity. The customer is the confidence gap, not the agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNodefault 10
statusNodefault open; resolved = anchored house resolutions; funded = agent-funded missions
subjectNofilter to one subject

Schema Changelog

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

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the free nature, the sources scanned, the output contents (ranked opportunities with estimates), and the pricing behavior (prices fall when prior evidence can be reused). It doesn't mention side effects, but this is inherently a read-only discovery tool.

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 somewhat lengthy but well-structured: it opens with a striking 'FREE: machine-generated demand,' then explains the source, the output, and a usage scenario. Each sentence adds value, though it could be tightened slightly.

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?

The tool has no required parameters and no output schema. The description explains what it returns (ranked opportunities with value estimates, confidence-gap estimates, suggested prices), the source of opportunities, and a concrete use case, making it complete for an agent to invoke correctly.

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?

The schema already covers 100% of parameters with descriptions. The description adds meaning by explaining that 'subject' refers to an entity of interest (supplier, product, dependency) and that prices vary based on prior evidence for that subject, which enriches the subject parameter's semantics.

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 tool discovers opportunities from public record events (FDA recalls, CPSC recalls, CVE/NVD revisions) and returns ranked opportunities with value estimates, confidence-gap estimates, and suggested prices. It also distinguishes itself from the sibling fund_opportunity by explicitly pointing to that tool for funding.

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?

The description provides clear context on when to use the tool (to find machine-generated demand/opportunities) and gives an explicit follow-up action: fund with fund_opportunity if the subject matters. However, it does not explicitly mention when NOT to use it or contrast with discover_capabilities.

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.

Resources