Skip to main content
Glama

AINumbers Fintech Intelligence Suite

by co.ainumbers
OverviewDiscussionsAdmin

Cash Forecast Accuracy Scoring

score_cash_forecast_accuracy
Read-onlyIdempotent

Cash Forecast Accuracy Scoring: OpenChainGraph compute node (analytics_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. Consumes upstream artifacts from: art-258-parse-camt053-reconciliation, art-261-test-hedge-effectiveness. Open at: https://ainumbers.co/chaingraph/art-263-score-cash-forecast-accuracy.html FV-status (published/proven/still-trusted for this spec): /fv-status/dfdd7083d50dfec46270d5ca2c4a06852efd98bdc1ae8d20e3d3b191e9b14ac7.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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pii_noteNo
by_horizonNo
table_sourceNo
table_versionNo
not_legal_adviceNo
overall_bias_pctNo
overall_mape_pctNo
regulatory_basisNo
total_observationsNo
afp_benchmark_tiersNo
skipped_zero_actualNo
timing_bias_detectedNo
overall_accuracy_tierNo
persistent_sign_periodsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "afp_benchmark_tiers": {
+      "type": "string"
+    },
+    "by_horizon": {
+      "properties": {
+        "T+1": {
+          "properties": {
+            "accuracy_tier": {
+              "type": "string"
+            },
+            "bias_pct": {
+              "type": "integer"
+            },
+            "mape_pct": {
+              "type": "integer"
+            },
+            "n": {
+              "type": "integer"
+            }
+          },
+          "type": "object"
+        }
+      },
+      "type": "object"
+    },
+    "not_legal_advice": {
+      "type": "string"
+    },
+    "overall_accuracy_tier": {
+      "type": "string"
+    },
+    "overall_bias_pct": {
+      "type": "integer"
+    },
+    "overall_mape_pct": {
+      "type": "integer"
+    },
+    "persistent_sign_periods": {
+      "type": "integer"
+    },
+    "pii_note": {
+      "type": "string"
+    },
+    "regulatory_basis": {
+      "type": "string"
+    },
+    "skipped_zero_actual": {
+      "type": "integer"
+    },
+    "table_source": {
+      "type": "string"
+    },
+    "table_version": {
+      "type": "string"
+    },
+    "timing_bias_detected": {
+      "type": "boolean"
+    },
+    "total_observations": {
+      "type": "integer"
+    }
+  },
+  "type": "object"
+}
  2. Added

TDQS

C2.7/5.0
Behavior4/5

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

Annotations declare readOnly/idempotent/non-destructive behavior, and the description adds useful details beyond that: deterministic execution, transient input processing with no storage/logging/retention, synthetic-input requirement, AP2 artifact export with execution_hash, and upstream chain dependencies. It still omits error/rate-limit behavior, but it meaningfully enriches the safety and provenance picture.

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

Conciseness2/5

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

The description is long and heavily boilerplate-laden, including a URL, FV-status hash, and offline verification note. The useful constraints are buried among infrastructure metadata, so many sentences do not earn their place for an agent selecting or invoking the tool.

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?

An output schema exists, so return values need not be explained, and the description covers compute behavior and upstream dependencies. However, for a scoring tool with policy_parameters, it does not describe what is being scored or how to supply the decision-function inputs beyond deferring to a manifest.

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 documents compute, parent_hashes, parent_tool_ids, and policy_parameters. The description reinforces the compute-mode semantics and names specific upstream artifacts, but adds little new meaning for how to set each parameter.

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 tool's name/title as 'Cash Forecast Accuracy Scoring' and calls it an 'OpenChainGraph compute node,' but never explains what the scoring actually computes or how it differs from the sibling compute_forecast_accuracy_score. It is essentially a tautological restatement plus infrastructure metadata.

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?

There is no guidance on when to use this tool versus alternatives such as compute_forecast_accuracy_score. The mention of compute modes and upstream artifacts is operational context, not usage selection guidance.

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

All AINumbers Fintech Intelligence Suite toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources