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AINumbers Fintech Intelligence Suite

EBA IM-Model Validation Tracker

compute_eba_im_model_validation_tracker
Read-onlyIdempotent

EBA IM-Model Validation Tracker: OpenChainGraph compute node (compliance_mandate). Deterministic OpenChainGraph compute node. By default (compute:"auto") inputs are computed server-side on Cloudflare Workers for gpu:false nodes with a registered kernel; compute:"browser" forces client-side execution and returns a browser delegation URL instead. gpu:true nodes always delegate to the browser. Inputs are processed transiently to compute the response and are not stored, logged, or retained. Use synthetic or anonymised inputs only. Exports an AP2 artifact with execution_hash for chain provenance. Open at: https://ainumbers.co/tools/674-eba-im-model-validation-tracker.html FV-status (published/proven/still-trusted for this spec): /fv-status/e5ebd9cab6d424d5a202b2144bf9dacc14abf4ed24f3f0ac3adbecdd87c14872.json — a snapshot, not a subscription; this receipt verifies offline regardless of whether that file is ever fetched.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
computeNoCompute mode (v0.4 Compute Binding). "auto" (default) = server for gpu:false nodes with registered kernels; "server" = force server-side; "browser" = always return browser delegation URL. gpu:true nodes always delegate.
parent_hashesNoexecution_hash values from upstream ChainGraph AP2 artifacts to chain from (sets chain.parent_hashes in the export).
parent_tool_idsNotool_id values matching parent_hashes, in the same order.
policy_parametersNoInput parameters for this tool's decision function. For gpu:false nodes with a registered kernel, these are computed server-side when compute is "auto" or "server". See the tool's manifest for field names.

Schema Changelog

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

  1. Added

TDQS

C2.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, it discloses determinism, server vs browser execution delegation, transient non-retention of inputs, the requirement to use synthetic data, AP2 artifact export with execution_hash, and FV-status snapshot semantics. These are substantive behavioral disclosures, and they do not contradict the annotations.

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

Conciseness3/5

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

The description carries useful runtime and privacy details but is wordy: 'OpenChainGraph compute node' appears twice in back-to-back sentences, and the URL/FV-status block adds provenance context at the cost of focus. It is not poorly organized, but it could be tightened without losing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and a domain-specific compliance purpose, the description omits what the EBA IM-model validation tracker actually evaluates or returns beyond an AP2 artifact. Execution modes, privacy, and verification are covered, but an agent would still be guessing about the tool's business function and expected policy_parameters.

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?

The input schema already describes all four parameters (schema coverage 100%), so the baseline applies. The description echoes compute-mode behavior but adds little beyond the schema for parent_hashes, parent_tool_ids, or policy_parameters; 'See the tool's manifest' in the schema is the only pointer to policy_parameters fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description restates the title ('EBA IM-Model Validation Tracker') and labels the tool as an 'OpenChainGraph compute node (compliance_mandate)' without stating what it actually computes or what action it performs. It mentions exporting an AP2 artifact but does not explain the tracker's decision function, so an agent cannot tell what this tool does versus the many model-validation siblings. This is closer to tautology than a functional definition.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives compute-mode behavior and a privacy directive ('Use synthetic or anonymised inputs only'), but never states when to choose this tool over alternatives such as assess_model_validation_status or run_model_test_battery. No exclusion or alternative-routing guidance appears anywhere.

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

C2.9/5.0
Disambiguation1/5

With 698 tools covering overlapping regulatory and compliance domains, many tools have near-identical names and purposes (e.g., check_genius_reserve_disclosure vs check_genius_reserve_disclosure_conformance, multiple DORA incident classifiers, several AP2 mandate validators). The highly templated descriptions further reduce distinctiveness, making reliable tool selection by an agent effectively impossible.

Naming Consistency4/5

The overwhelming majority of tools follow a consistent snake_case verb_noun pattern (assess_*, build_*, compute_*, validate_*, verify_*). Minor deviations exist (camt053_parse, workbook_evaluate, ha_gate_status, sdjwt_issue, etc.), but they are a small fraction of the total and follow recognizable domain-prefix conventions.

Tool Count1/5

698 tools is an extreme oversizing for any server, far beyond the 50+ threshold for a low score. Even with dedicated search/discovery tools, this unwieldy surface guarantees cognitive overload, high misselection risk, and severe practical usability problems.

Completeness4/5

The suite covers an extraordinarily broad range of fintech/regulatory domains — capital adequacy, AML, payments, crypto, AI governance, trade finance, and many verification/recompute lifecycles. Obvious gaps are difficult to identify, though the set is not a coherent single lifecycle and some niche areas are inevitably absent.