Relaystation

ask_operator

Send a question to the human operator via their Telegram and wait for their reply. Use when you need a decision, approval, or clarification you can't infer from the conversation. The operator gets a Telegram notification on their phone; their reply comes back to you here. Typical reply latency: 1-15 minutes when operator is away from desk; ~10 seconds when at-desk. Cost: $0.005 above the daily free tier (5 free/day). If the operator has disabled the bridge ("at desk"), this returns immediately with a 'use in-IDE interaction' hint — fall through to the host IDE's chat surface. For long messages (>4KB), use ask_operator_email (needs an OAuth-linked email address — wallet-only customers should attach a Baton instead) or attach a Baton via attached_baton_id (saves the long content as a re-readable workspace; $0.01). ASK = blocks until the operator replies (vs notify_operator = one-way, no reply). Example: ask_operator {message:'Deploy to prod now?'}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`message`	Yes
`max_wait_seconds`	No
`attached_baton_id`	No

TDQS

A4.7/5.0

Behavior5/5

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

No annotations provided, so description carries full burden. It discloses latency (1-15 min, ~10 sec), cost ($0.005, 5 free/day), blocking nature, and behavior when operator is away or disabled.

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?

Description is well-structured and front-loaded, but contains several pieces of information (cost, latency, alternatives) that could be slightly more concise. Still efficient.

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?

Given no annotations or output schema, description covers purpose, usage, behavior, cost, latency, error handling. Lacks explicit return value description, but it's implied. Good overall completeness.

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

Parameters4/5

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

With 0% schema description coverage, description compensates well for message and attached_baton_id (explains cost and use case). However, max_wait_seconds is not explicitly tied to the parameter, relying on implicit understanding. Still adds significant value.

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?

Description clearly states the tool sends a question to a human operator via Telegram and waits for a reply. It distinguishes from sibling notify_operator and fallback options.

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

Usage Guidelines5/5

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

Explicitly says when to use (need decision/approval/clarification) and when not (if operator disabled, use in-IDE; for long messages, use ask_operator_email or attach Baton). Also contrasts with notify_operator.

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

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct action and resource, such as baton operations, agent messaging, contracts, and account management. There is no overlap in functionality, making it clear which tool to use for each task.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case (e.g., append_to_baton, create_baton, message_agent). The verbs are descriptive and uniform, aiding predictability.

Tool Count5/5

With 14 tools, the server covers its domain (agent coordination, batons, contracts, tool discovery) without being overbearing. The count is well-scoped for a platform offering a variety of operations.

Completeness3/5

The tool set covers core operations like creating, reading, and appending to batons, as well as messaging and contracts. However, it lacks list or delete operations for batons and contracts, which are notable gaps for a complete lifecycle.

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