Describe a formula
canon_describe_formulaInput schema with ranges and defaults, output names, stated convention, citations and test vectors for one formula.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| formula | Yes |
canon_describe_formulaInput schema with ranges and defaults, output names, stated convention, citations and test vectors for one formula.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 declare readOnlyHint=true, which the description does not contradict. The description adds useful context about what the tool returns (ranges, defaults, output names, convention, citations, test vectors), but it does not disclose other behavioral traits such as error handling, authentication needs, or limiting behavior beyond the read-only nature.
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?
The description is a short single sentence with no wasted words, but it is a sentence fragment rather than a full grammatical sentence. The key action verb is missing, and the most important information ('for one formula') is placed at the end, making it less immediately scannable than it could be.
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?
Given the lack of an output schema, the description's list of returned contents is helpful. However, it omits the core action the tool performs and provides no connection to sibling tools or guidance on input selection. An agent can infer the basic operation from the title, but the description is not complete enough on its own for confident invocation.
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 has one parameter with 0% description coverage, so the description must compensate. It only says 'for one formula' and does not explain how to specify the formula, what the enum values represent, or how to select among them. The schema's enum names are self-descriptive, but the description adds little beyond what the schema already provides.
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 conveys that the tool provides details for a single formula, including input schema, output names, convention, citations, and test vectors. The title 'Describe a formula' supplies a clear verb and resource, and the phrase 'one formula' distinguishes it from listing all formulas, though not from computing or citing formulas explicitly.
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?
There is no guidance on when to use this tool versus siblings like canon_compute, canon_list_formulas, or canon_cite. The description does not mention use cases, exclusions, or alternatives, leaving the agent to infer when this tool is appropriate.
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.