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get_transactions

Read-only

Fetch the full transaction (modification) history for an award.

Every modification, option exercise, and de-obligation is a transaction.
Modification number '0' is the original base award. Use to understand
the full lifecycle of a contract including its descriptive text at each
point in time.

Returns per transaction: id, type, action_date, action_type,
modification_number, description, federal_action_obligation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "generated_award_id": "<generated_award_id>"
    },
    "name": "get_transactions"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageNo
sortNoaction_date
limitNo
orderNoasc
generated_award_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

While annotations handle the safety profile (readOnlyHint: true), the description adds crucial domain behavior by explaining that 'Modification number '0' is the original base award.' This provides necessary context on the data model lifecycle that is not found in the schema or 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 front-loaded with the core purpose and then builds out the explanatory context. It is efficient, though the final bullet-style list of return fields adds a slight amount of necessary bulk.

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

Completeness5/5

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

The description is extremely complete regarding the domain model. It explains the full scope of the data captured (modification, option exercise, de-obligation) and describes the structure of the output data fields, fulfilling the goal of explaining the tool's function beyond structured fields.

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?

Since the input schema coverage is 0%, the description is under pressure to explain the parameters. It successfully defines the output fields, but it fails to add semantic meaning to the input parameters like 'page', 'sort', or 'limit', adhering to the baseline score for lacking input specifics.

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

Purpose5/5

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

The description is exceptionally clear, using the specific verb 'Fetch' and clearly identifying the resource as 'full transaction (modification) history for an award.' It explicitly defines what constitutes a transaction (modification, option exercise, de-obligation), making it highly distinguishable from general list tools.

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

Usage Guidelines4/5

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

The tool is clearly defined as providing a complete history for a single award, which establishes its specific use case. It doesn't exclude other possible use cases, but the high specificity of the history retrieval makes its intended use highly visible to the agent.

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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