canon_verifyRecompute a receipt from its inputs and confirm its id and outputs. Use it to check a number another agent or document claims came from this service.
| Name | Required | Description | Default |
|---|---|---|---|
| receipt | Yes | The receipt object returned by canon_compute |
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 mark it read-only and idempotent, so the description's added behavior — recomputing from inputs and confirming id/outputs — is meaningful and consistent. It gives the agent a concrete sense of what the tool does beyond the safe-operation hints, without contradicting them.
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 short sentences, with the core behavior front-loaded and no filler. The purpose and the intended usage are each stated in one efficient sentence.
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?
For a one-parameter, read-only verification tool with full schema coverage, the description covers the invocation reason and core behavior. The main gap is not spelling out the return value on success or failure, but this is relatively minor given the simple, annotation-safe context.
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?
The input schema fully describes the only parameter, the receipt object returned by canon_compute. The description adds only a minor clue that the receipt contains the inputs needed for recomputation, which does not materially go beyond schema coverage.
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 states a specific action — recompute a receipt and confirm its id and outputs — and clarifies it is for checking values claimed to have come from the service. It is clear, though it does not explicitly differentiate from canon_compute or other siblings by name.
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?
It gives an explicit use case: checking a number another agent or document claims came from this service. It does not state when not to use it or name alternative tools, but the context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.