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SACS.ai MCP — California School District Financial Data

sacs_compare_districts

Read-onlyIdempotent

Compare deterministic object-category totals for two explicit official datasets.

Input Schema

NameRequiredDescriptionDefault
client_idNo
fund_codesNo
resource_codesNo
base_dataset_refYes
compare_dataset_refYes
base_district_identifierYes
compare_district_identifierYes

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

C2.7/5.0
Behavior2/5

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

Annotations already cover safety (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false). The description adds only 'deterministic' and 'official' without explaining return behavior, error conditions, rate limits, or any operational constraint beyond the annotations.

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 front-loaded sentence with no wasted words, so it is appropriately concise. However, its terseness is part of the problem: it is short but not sufficiently informative for a 7-parameter comparison tool.

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

Completeness2/5

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

Although an output schema exists and annotations cover safety, the input side is complex (7 parameters, 4 required) and schema description coverage is 0%. A one-sentence description does not provide enough guidance on required identifiers, dataset refs, optional scoping, or comparison semantics.

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?

With 7 parameters and 0% schema description coverage, the description provides no parameter-level semantics. It does not explain base vs compare district/dataset refs, the default behavior for fund_codes, what resource_codes mean, or the role of client_id.

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 states a specific verb ('Compare') and resource ('object-category totals') for two datasets, which makes the core action clear. It does not, however, differentiate this tool from siblings like sacs_query_financial_summary, and the name says 'districts' while the description says 'datasets', leaving a mild ambiguity.

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 implies use when comparing two explicit official datasets, but gives no explicit when-to-use guidance, no exclusions, and no named alternatives among the many sibling tools. An agent must infer the appropriate context.

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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