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Ship On Friday Check

ship_on_friday_check
Read-onlyIdempotent

Check whether you should ship on Friday. The answer is always no. Returns a rotating reason, risk level (always catastrophic), suggested day, and on-call sympathy score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
is_fridayNoOverride Friday detection (auto-detected by default)
team_sizeNoSize of your team
deploy_typeNoType of deploy: hotfix, feature, or refactor

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoRotating reason why you shouldn't ship on Friday
risk_levelNoRisk level assessment (always catastrophic)
suggested_dayNoRecommended day to deploy instead
on_call_sympathy_scoreNoSympathy score for on-call engineer (0-100)

TDQS

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: 'The answer is always no', 'Returns a rotating reason, risk level (always catastrophic), suggested day, and on-call sympathy score.' This explains the output behavior. Annotations already indicate safety (readOnlyHint, idempotentHint, destructiveHint false). No contradiction.

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 extremely concise with only three sentences. The purpose is front-loaded in the first sentence. Every sentence contributes value: purpose, answer, return fields. No wasted words.

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?

Given the tool's simplicity and the presence of an output schema (not shown but indicated), the description adequately explains the tool's behavior. It lists the return fields and the constant answer. For a novelty check tool, this is complete.

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?

The input schema has 100% description coverage for all 3 parameters (is_friday, team_size, deploy_type). The tool description does not add any additional meaning about these parameters beyond what the schema already provides. Baseline score of 3 is appropriate.

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 clearly states the tool's purpose: 'Check whether you should ship on Friday.' It includes the specific verb 'check' and the resource 'shipping on Friday'. The humorous twist 'The answer is always no' adds clarity. It distinguishes itself from the many sibling tools by its unique function.

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 description implies when to use (when considering shipping on Friday) and that the answer is always no, but does not explicitly state when not to use or provide alternatives. Given the tool's novelty and humorous nature, the implied usage is clear enough.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among data query tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, and recent_changes. However, detailed descriptions and different use cases help an agent distinguish them, so it is mostly clear.

Naming Consistency5/5

All tool names follow a consistent lower_snake_case pattern with a verb_noun style (e.g., ask_pipeworx, list_subscriptions, validate_claim). There are no mixed conventions, making it predictable and easy to understand.

Tool Count4/5

With 31 tools, the server covers a broad domain of data queries, prediction markets, memory, and subscriptions. While slightly more than typical, each tool earns its place and the count is reasonable for the comprehensive platform scope.

Completeness5/5

The tool surface is extensive, covering data querying, analysis, entity resolution, comparison, change tracking, memory, subscriptions, and more. There are no obvious gaps; it supports a wide range of user intents for the server's purpose.