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Glama

DPX — Institutional Cross-Border Settlement

by io.github.untitledfinancial

esg.batch

Read-only

Screen up to 50 entities in a single call. Accepts LEIs or company names (GLEIF-resolved). Returns results ranked by composite ESG score descending — highest scoring counterparties first. Useful for portfolio-level compliance screening, supplier due diligence, and TMS pre-payment checks. Name resolution is slower than direct LEI input.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leisNoArray of LEIs to screen (fastest path — no GLEIF resolution needed).
namesNoArray of company names to screen (resolved via GLEIF — slower, allows ≤3s per name).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalNo
failedNo
resultsNoEntities sorted by composite score descending. Each item includes lei, entityName, score object, or an error note.
succeededNo

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only and non-destructive behavior. The description adds valuable context: the 50-entity batch limit, ranking order, and the performance trade-off of name resolution vs. direct LEI input. No contradictions with annotations.

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?

The description is four concise sentences with no redundant text. It front-loads the core capability and each sentence contributes useful details about limits, inputs, output ranking, use cases, or performance.

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 present and annotations covering the safety profile, the description provides sufficient context including purpose, inputs, output ordering, use cases, and performance characteristics. It is complete for a batch screening tool of this complexity.

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 descriptions already cover both parameters at 100%, but the description adds meaningful nuance: LEIs are the fastest path and names require slower GLEIF resolution. This goes beyond the baseline schema information.

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 screens up to 50 entities in a single call and returns results ranked by composite ESG score. It distinguishes itself from sibling tools by explicitly positioning it for batch/portfolio-level screening rather than single-entity lookups.

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 names specific use cases: portfolio-level compliance screening, supplier due diligence, and TMS pre-payment checks. It does not explicitly mention alternatives for single-entity checks, but the context is clear enough to guide the agent.

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

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