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AINumbers Fintech Intelligence Suite

by co.ainumbers

Diff an original vs. proposed revision and mint a diff receipt

redline_diff
Read-onlyIdempotent

An agent proposes an edit: diffs original text/markdown against its own revised version and returns the line-level hunks plus a diff receipt (original/revised digests, diff-algorithm declaration, per-hunk digests). Byte-identical to what the tools/552 browser workbench computes for the same inputs -- a human can paste the SAME original/revised text into that workbench, disposition each hunk (accept/reject/comment), and their resulting hunk/disposition receipts will reference this diff_receipt's execution_hash. Pass the whole bundle (this diff_receipt + the human's hunk_receipts + disposition_receipt) to redline_verify to check the interleaved chain end-to-end.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
revisedYesProposed revised text or markdown document.
originalYesOriginal text or markdown document.
generated_atNoISO 8601 timestamp (caller-supplied for determinism). Defaults to the call time.

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds valuable context: byte-identical behavior with a workbench, deterministic execution_hash, and interleaved receipt chain. This goes beyond what annotations provide, though it omits potential error handling or authorization detail.

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 a single paragraph that efficiently conveys key points without redundancy. While not overly verbose, it could be slightly better organized by separating the workflow instructions from the behavior note. Still, every sentence earns its place.

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

Completeness4/5

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

Given the tool has 3 parameters, no output schema, and moderate complexity, the description explains the output format, interop with a workbench, and the receipt chain. It does not address error conditions or detailed field descriptions, but it is sufficient for typical use.

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 all parameters. The description does not add new parameter-level semantics but provides overall context. 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?

The description clearly identifies the tool's action (diff), the resources (original vs revised text/markdown), and the output (line-level hunks and diff receipt). It also distinguishes itself from the sibling tool `redline_verify` by explicitly stating the downstream use.

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 specifies the tool's purpose for proposing an edit and explains the next step with `redline_verify`. While it does not list explicit exclusion criteria, the context is clear and relevant for an AI agent to understand when to use this tool.

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

C2.4/5.0
Disambiguation1/5

Nearly every tool is an 'OpenChainGraph compute node' with identical boilerplate, and dozens of assess/check/validate/verify/lint/recompute verbs overlap heavily in purpose. An agent cannot reliably tell which of many similarly scoped tools should handle a given compliance or analytics question.

Naming Consistency2/5

Most names are snake_case, so there is superficial consistency, but the verb vocabulary is enormous and unpredictable, mixing build/compute/check/validate/verify/lint/assess/classify/score/reconcile and more. Several noun-first names such as pain001_validate, recon_match, and ha_record_validate further break the pattern.

Tool Count1/5

695 tools is far beyond any practical agent-facing surface, exceeding even the extreme end of the calibration range. This is a full product catalog dumped into one MCP server rather than a curated, usable tool set.

Completeness3/5

The suite covers an extremely wide range of fintech and regulatory calculators, validators, chain tools, and discovery utilities, so coverage is broad rather than thin. However, the lack of a clear domain boundary makes completeness nearly impossible to assess, and the heavy overlap suggests the surface was generated rather than intentionally designed.

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