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company_board_pack

Generate a monthly board pack from financial data including revenue, payables, retention, cash flow, runway, exceptions, and decisions, presented as JSON with rendered markdown.

Instructions

The month's board pack from the persisted periods, revenue and payables chains, and retention cases: revenue, spend, books verified, cash proven in and out, runway, exceptions, decisions and their counterfactuals. JSON with rendered markdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nowNo
monthYes
evals_jsonNo
project_idYes
bundle_jsonYes
forecast_jsonNo
decisions_jsonNo[]
exceptions_jsonNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.1.1

TDQS

D1.9/5.0
Behavior2/5

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

The description only discloses that the output is 'JSON with rendered markdown' and hints that decisions and exceptions can be provided as inputs. It does not state whether the tool is read-only, whether it aggregates or transforms data, or any side effects or performance characteristics.

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

Conciseness3/5

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

The description is brief at two sentences, but it packs many concepts into a run-on list that is difficult to parse. A clearer structure separating purpose, inputs, and output would improve readability without adding much length.

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

Completeness1/5

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

Given 8 parameters, 3 required, no annotations, and a complex domain, this description is far too sparse. It does not explain required fields, how parameters interact, what the output structure contains, or any constraints, leaving major gaps for an agent to operate correctly.

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

Parameters1/5

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

The schema lists 8 parameters with no descriptions, and the tool description does not explain any of them. Terms like 'bundle_json', 'evals_json', and 'forecast_json' are left entirely to interpretation, so the agent has no semantic grounding for these inputs.

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

Purpose3/5

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

The description names the deliverable ('board pack') and lists its contents (revenue, spend, exceptions, decisions), but it lacks an explicit action verb and uses vague jargon like 'persisted periods' and 'cash proven in and out'. It is understandable but not fully crisp about what the tool does.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool versus the many sibling tools such as company_revenue, company_payables, or company_brief. The description does not explain what scenarios call for the board pack or how it differs from querying individual metrics.

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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