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SACS.ai MCP — California School District Financial Data

sacs_query_financial_summary

Read-onlyIdempotent

Return bounded aggregate financial summaries for one explicit official dataset.

Input Schema

NameRequiredDescriptionDefault
client_idNo
dataset_refYes
group_limitNo
fund_code_maxNo01
fund_code_minNo01
goal_code_maxNo
goal_code_minNo
object_code_maxNo
object_code_minNo
function_code_maxNo
function_code_minNo
resource_code_maxNo
resource_code_minNo
district_identifierYes
include_zero_categoriesNoInclude zero-amount object category and group rows.

Output Schema

NameRequiredDescriptionDefault
okYes
dataNo
errorNo
request_idYes
limitationsNo
data_versionYes
contract_versionNosacs.mcp.v1
source_provenanceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so safety is covered. Beyond that the description adds only the word 'bounded', which hints at aggregation caps (group_limit max 25) without explaining what is bounded, how amounts are aggregated, or what happens when code ranges are omitted.

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?

A single front-loaded sentence with no filler. It is efficient, but the brevity is achieved by omitting information rather than by compressing it.

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?

Invalid: a 15-parameter tool with 7% schema coverage and a single-sentence description leaves large gaps around filter pairing, defaults (Fund 01) and aggregation behavior. Annotations and an output schema cover the safety and return-shape burdens, but the input side remains under-explained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 7% — just three of fifteen parameters (fund_code_min/max, include_zero_categories) carry any documentation. The description does not explain district_identifier, the 'official:<id>' form of dataset_ref, the min/max code-range pairing pattern, or group_limit and defaults; 'one explicit official dataset' and 'bounded' are only faint hints.

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 names a specific verb ('Return') and a specific resource ('aggregate financial summaries'), scoped to 'one explicit official dataset'. That is enough to separate it from siblings like sacs_compare_districts or sacs_query_attendance, though it never names an alternative explicitly.

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?

There is no guidance on when to choose this over sacs_compare_districts or sacs_district_context_pack, and no mention of prerequisites such as needing a registered client or a dataset ref obtained from sacs_list_public_datasets. The agent must infer usage entirely from the name and schema.

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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