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Glama

DPX — Institutional Cross-Border Settlement

by io.github.untitledfinancial

intelligence.contagion

Read-onlyIdempotent

Contagion Intelligence — simulates how a macro or financial shock spreads through 30 nodes across 6 domains (financial systems, real economies, commodity networks, policy anchors, social systems, physical infrastructure) using an epidemiological R-value model. Returns system R trajectory, per-epoch spread map, superspreader nodes, containment forecast, and AI briefing. R < 1.0 = self-limiting; R ≥ 1.0 = expanding. Call /contagion/nodes first to discover valid origin IDs. POST with origin and magnitude.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originNoOrigin node ID. Call intelligence.contagion with listNodes:true to discover valid IDs.
listNodesNoIf true, returns all valid origin node IDs instead of running a simulation.
magnitudeNoInitial shock magnitude 1–100.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
systemRNoSystem-level R value. ≥1.0 means spreading.
spreadMapNoPer-epoch infection state across all nodes.
synthesisNo
containmentNoForecast of when/if containment is achieved.
superspreadersNoNodes with highest R contribution.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

With annotations already marking readOnlyHint=true and destructiveHint=false, the description adds valuable behavioral context: the R-value interpretation (R < 1.0 vs ≥ 1.0), concrete return items (R trajectory, spread map, superspreader nodes, containment forecast, AI briefing), and a prerequisite call. This goes far beyond the structured annotations.

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 ~70 words across four sentences, front-loaded with the primary purpose. Each sentence adds substantive information (model, output, interpretation, prerequisite), though it could be slightly restructured for scannability.

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?

With an output schema and safety annotations present, the description provides a full picture: the model, domain coverage, R-value threshold, output items, and prerequisite call. This is more than sufficient for an agent to decide and invoke correctly.

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

Parameters3/5

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 a workflow hint (call listNodes first) and says 'POST with origin and magnitude', but does not add additional semantic meaning beyond the schema's parameter 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 opens with 'simulates how a macro or financial shock spreads' — a specific verb and resource — and details unique aspects (30 nodes, 6 domains, R-value model), distinguishing it from sibling intelligence tools.

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?

It explicitly instructs to 'Call /contagion/nodes first to discover valid origin IDs' and notes 'POST with origin and magnitude', providing a clear workflow. However, it does not name alternative tools for similar contagion simulations, so no explicit when-not-to-use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All DPX — Institutional Cross-Border Settlement toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple stablecoin routing options (route vs stability.stablecoin_route), several compliance pre-checks (flow_check, policy.check, mercury.ach_authorize), and numerous FX/stability tools (oracle.stability, stability.corridor, market.fx, fx.rate). Even with detailed descriptions, the boundaries are subtle and an agent could easily select the wrong tool.

Naming Consistency3/5

The dot-separated namespace convention is mostly consistent and readable, but verb vs noun usage varies (e.g., settlement.execute vs batch_settle vs route). Subscription tools also mix forms (intelligence.subscribe vs intelligence.subscription.get/delete), showing minor inconsistency.

Tool Count1/5

81 tools is an extreme count for a settlement server. Even accounting for the broad 'institutional' scope, the volume overwhelms the core purpose and creates a heavy cognitive load for agents, far beyond the typical 3-15 well-scoped tool set.

Completeness3/5

The core settlement lifecycle is well-covered (quote, execute, track, receipt, batch), but there are notable gaps such as missing policy update/delete and no receipt retrieval (only create). While many tangential domains are over-covered, certain CRUD operations are absent, creating dead ends.

Resources