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boardroom-agents

boardroom-agents

by edgemoorlf
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

ask_board

Read-only

Ask natural-language questions about board history. Returns answers with governance verdicts and triggered policy rules.

Instructions

Ask a natural-language question about the board's history. The answer is drafted by a research agent, then reviewed against the board's answer policy before it is returned. Includes the governance verdict and any policy rules that fired.

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": {
      "question": "<question>"
    },
    "name": "ask_board"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=true), the description adds significant behavioral context: the answer is drafted by a research agent, reviewed against an answer policy, and includes governance verdict and policy rules. This fully informs the agent about the tool's internal process.

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?

Two sentences, no redundancy. The first sentence states the core purpose, and the second adds valuable process details. Efficiently front-loaded.

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 the tool's complexity (natural-language Q&A), the description covers purpose, process, and output. However, it lacks guidance on question formulation or potential limitations (e.g., response time due to agent review), which would enhance completeness.

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?

The input schema has 0% description coverage, and the tool description does not elaborate on the 'question' parameter (e.g., format, scope, examples). The description fails to compensate for the missing schema documentation.

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: asking natural-language questions about the board's history. It distinguishes from sibling tools (e.g., list_meetings, search_documents) by focusing on open-ended Q&A rather than structured retrieval.

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 (for natural-language queries about board history) but does not explicitly state when not to use or provide alternatives. The sibling context helps, but explicit guidance would improve this dimension.

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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