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Glama

DPX — Institutional Cross-Border Settlement

by io.github.untitledfinancial

compute.cost

Read-onlyIdempotent

Get a model recommendation for a task type without running inference. Returns the best free model for the task, its strengths and speed tier, and a list of alternatives. Use this when an agent needs to select a model before committing to inference, or to surface model selection logic to a human. Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesDescription of the task — e.g. "summarize a financial document", "write Python code", "translate from French", "reason through a math problem".
speedNotrue = prefer fastest model over most capable. Default false.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds behavioral context: 'without running inference' and 'Free', which goes beyond the annotations and clarifies side-effects and cost characteristics.

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

Conciseness5/5

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

Three sentences with the first sentence front-loading the core purpose. Every sentence adds value: purpose, use case, and cost note. No filler.

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?

For a simple 2-parameter tool with rich annotations and no output schema, the description sufficiently covers return values (best free model, strengths, speed tier, alternatives) and use context. It is complete for agent selection.

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 the schema already explains both parameters. The description doesn't add parameter-specific syntax or format details beyond what the schema provides. Baseline 3 is appropriate.

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?

Description clearly states it gets a model recommendation for a task type without running inference, listing specific outputs (best free model, strengths, speed tier, alternatives). This distinguishes it from siblings like compute.models and compute.route.

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?

Explicit when-to-use guidance is provided: 'Use this when an agent needs to select a model before committing to inference, or to surface model selection logic to a human.' It implies not for running inference but doesn't name specific alternative tools or exclusions.

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