Canon (wallstreet.wiki)

Compute many formulas at once

canon_batch

Read-onlyIdempotent

Run up to 50 computes in one call, for scenario grids and sensitivity tables. Each item returns its own receipt; the batch receipt id is sha256 over the sorted item receipt ids, so a whole table can be cited by one hash.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "items": "<items>"
    },
    "name": "canon_batch"
  }
}

Input Schema

Name	Required	Description	Default
`items`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 6, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already mark the tool readOnly and idempotent; the description adds genuinely useful behavioral detail by revealing that each item returns its own receipt and the batch receipt id is a sha256 over sorted item receipt ids. This explains deterministic output/reference behavior beyond 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.

Conciseness5/5

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

Two dense sentences with the key detail ('up to 50 computes', per-item receipts, batch hash) front-loaded. Every sentence adds information and there is no filler.

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?

For a tool with a nested items array, formula enum, and no output schema, the description covers the essential output/citation semantics without explaining error behavior. It is complete enough for correct invocation, though a note on partial failures or per-item validation would make it fully robust.

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?

With 0% schema description coverage, the description partially compensates by explaining that items are computes, up to 50, and each yields a receipt. It does not, however, break down the required formula/inputs structure, leaving the agent to infer those from the schema field names and sibling tools.

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?

States a specific batch operation: run up to 50 computes in one call for grids/sensitivity tables. The description distinguishes the tool from single-compute siblings (canon_compute) by emphasizing batching and the single hash for a whole table.

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?

Describes when to use it: scenario grids and sensitivity tables requiring many computes. It does not explicitly name canon_compute as the alternative for a single compute, but the guidance is clear enough for an agent to pick the right 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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