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Calera FINSEC — Certified SEC Memory

Certified Financial Arithmetic

lattice_arith_evaluate
Read-only

Deterministic zero-error algebraic solver (cagr, ratio, multiply, divide, yoy_series, product_chain) over verified SEC EDGAR financial metrics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opYescagr | ratio | multiply | divide | yoy_series | product_chain
metricNoEDGAR metric, e.g. free cash flow, revenue
companyNoTicker or name when fetching EDGAR cells
factorsNoOptional comma-separated positive factors for product_chain, e.g. 0.79,0.30,1.07,4.5
periodsNoOptional comma-separated fiscal periods for yoy_series/product_chain, e.g. FY2020,FY2021,FY2022
end_periodNoEnd / denominator period, e.g. FY2023
low_tokensNoOptional. When true, returns compact JSON schema.
start_periodNoStart / numerator period, e.g. FY2020

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
stNoCompact status indicator (OK | REFUSE)
perNoCompact period string
valNoCompact numeric value
hashNoCryptographic filing audit hash
cellsNoValuation pack verified cells dictionary
valueNoVerified financial metric numeric value or computed arithmetic result
periodNoFiscal reporting period
reasonNoExplanation when SAFE_REFUSAL is returned
statusNoVerification status: VERIFIED_SUCCESS | SAFE_REFUSAL | COMPLETE | OK
companyNoCompany name or ticker symbol
conceptNoUS-GAAP / XBRL financial concept
operandsNoUnderlying verified metric operands with accessions
formattedNoFormatted monetary or percentage string
provenanceNoSEC EDGAR filing provenance details

TDQS

B3.2/5.0
Behavior3/5

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

The annotations already declare readOnlyHint=true, so the agent knows this is a read-only computation. The description adds that the solver is 'deterministic' and 'zero-error' and operates over 'verified' metrics, which provides modest extra context beyond the annotation. However, it does not disclose authentication needs, rate limits, or data sourcing details.

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 sentence that efficiently conveys the core purpose. It front-loads 'Deterministic zero-error algebraic solver' and lists operations. It is concise, though it could be slightly more structured for readability.

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

Completeness3/5

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

Given the tool has 8 parameters (1 required, 7 optional dependent on the operation) and an output schema, the description is too sparse. It does not explain that the 'metric', 'company', 'periods', etc. parameters are conditional on the chosen 'op'. The output schema exists, so return values are covered, but operational context is missing.

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 baseline is 3. The description lists the operation names in parentheses but does not add any meaning beyond what the schema already provides for each parameter. No parameter-specific semantics are introduced.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs arithmetic operations (cagr, ratio, multiply, divide, yoy_series, product_chain) over verified SEC EDGAR financial metrics. It uses a specific verb ('algebraic solver') and resource, but does not explicitly differentiate from sibling tools like compute_sec_cagr.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings (e.g., compute_sec_cagr, query_financial_sec). It does not mention prerequisites, contextual triggers, or when not to use it.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes: CAGR calculation, general algebraic solving, metric querying, sector peer retrieval, and valuation inputs. However, `query_financial_sec` and `query_sec_metric_exact` overlap significantly in returning verified SEC facts, potentially causing confusion about which to use for a given retrieval need.

Naming Consistency3/5

Tool names use mixed prefixes: `query_` for three tools, but `compute_`, `lattice_arith_`, `valuation_`, and `vln_` for others. No single verb-noun pattern is maintained across the set, though names are still readable and descriptive.

Tool Count5/5

Seven tools is a well-scoped number for a specialized financial data server. Each tool serves a clear function without redundancy, and the count is neither too thin nor too heavy for the domain of certified SEC metrics and arithmetic.

Completeness5/5

The tool surface covers the full lifecycle of querying certified SEC data, performing deterministic calculations, retrieving sector peers, and obtaining valuation inputs. An overview tool helps agents understand capabilities. No obvious gaps are present for the stated purpose.