canon_computeRun one formula from the registry on validated inputs. Returns outputs, the convention used, citations, and a receipt whose id is sha256 over formula, version, canonical inputs and outputs. Invalid inputs return an explicit problem list and a working example instead of nulls.
| Name | Required | Description | Default |
|---|---|---|---|
| inputs | Yes | Inputs keyed by name; see canon_describe_formula | |
| formula | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and idempotentHint, but the description adds meaningful behavioral details: the receipt id is sha256 over formula, version, canonical inputs and outputs, and invalid inputs return a problem list plus a working example rather than nulls. This goes well beyond the annotations and explains failure behavior and return semantics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two information-dense sentences with no filler. The main action is front-loaded, followed by return artifacts and invalid-input behavior. Every clause contributes useful guidance for an agent deciding whether and how to invoke the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has only two required parameters, and the description covers the core invocation flow, return contents, receipt construction, and error response behavior. Since there is no output schema, the description does the necessary work of telling the agent what to expect. It also points to canon_describe_formula for input details, making the definition complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%; the schema documents the 'inputs' property and points to canon_describe_formula, while the formula enum is self-explanatory as a list of registered formulas. The description adds the concepts of 'validated inputs' and 'canonical inputs' but does not explain specific parameter formats or how inputs map to formulas. It maps to the schema and augments it somewhat without fully compensating for the missing details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Run one formula from the registry on validated inputs.' It clearly identifies what the tool computes and differentiates itself from the sibling canon_batch by emphasizing 'one formula' rather than batch operations. It also conveys the distinctive return artifacts (outputs, convention, citations, receipt), making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description sets clear context: use this tool to run a single registered formula with validated inputs. The phrase 'one formula from the registry' implies a scope and avoids confusion with batch or listing tools. It does not explicitly name alternatives or conditions for when not to use it, but the context is clear enough for correct selection.
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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Each tool has a clearly distinct job: searching, fetching entries, mapping entities, running formulas, batching computations, verifying receipts, citing, listing sites, monitoring changes, and looking up regulatory thresholds. Even the related compute/batch/verify tools are cleanly separated by single vs. batch execution and verification responsibility.
All tools share the canon_ prefix and use lowercase snake_case, which makes the set feel consistent and predictable. However, the suffixes mix bare resource nouns (canon_sites, canon_thresholds, canon_entity) with verb phrases (canon_describe_formula, canon_list_formulas, canon_get_entry), so the naming convention is not perfectly uniform.
Thirteen tools is well within the ideal range for a reference-and-computation API, and each tool covers a distinct capability without redundancy. The count feels proportionate to the breadth of the finance canon domain.
The surface is complete for a read-only reference service: search, entry retrieval, entity resolution, contract lookup, formula inspection, computation, batch execution, receipt verification, citations, change monitoring, site stats, and regulatory thresholds. There are no obvious dead ends or missing core operations.