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by faxik
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Python
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milestone_audit_query

Query milestone audit logs with filters for milestone, item, actor, and time. Returns most-recent entries first.

Instructions

Audit log query with filters. Returns most-recent rows first.

Args:

  • milestone_id: Filter by milestone slug.

  • item_ref: Filter by item id.

  • actor: Filter by actor.

  • since: ISO datetime — only rows at or after this time.

  • limit: Max rows (default 200). 0 means NO rows; a negative value is an error (it used to mean "no limit").

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "milestone_audit_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
actorNo
limitNo
sinceNo
item_refNo
milestone_idNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does a solid job: it discloses sort order, datetime filtering semantics, and the unusual limit edge cases where 0 returns no rows and negative values are errors. This adds meaningful runtime behavior beyond the schema, though it does not mention whether filters combine or any access requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and immediately front-loads the core purpose before moving into a clean bulleted parameter list. Every line adds information, and the historical note about limit is placed exactly where relevant without extra verbosity.

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?

For a 5-parameter optional filter query with an output schema present, the description covers all parameters, ordering, and edge cases. The main gap is that it does not state whether multiple filters are combined with AND semantics, which could matter to an agent planning a query with several filters.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate, and it fully does. Every parameter is explained with meaningful semantics: milestone_id is a slug, item_ref is an item id, since is an ISO datetime with at-or-after semantics, and limit gets defaults plus edge-case behavior.

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 opens with a specific verb plus resource: 'Audit log query with filters,' and adds concrete behavior ('Returns most-recent rows first'). It clearly identifies this as the audit-log querying tool, which distinguishes it from sibling query tools like blockers_query, reqs_query, and relations_query.

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

Usage Guidelines3/5

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

Usage is only implied by the tool name and introductory phrase; an agent can infer it should be used when filtering audit log entries. However, the description gives no explicit guidance about when to prefer this over other query tools, nor any exclusions or alternatives.

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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