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Glama

company_services_margin

Calculate sealed services margin per engagement, role, and worker: cap revenue at contract, apply bill/cost rates, and surface margin, burn, value at risk, blended rates, availability, utilisation.

Instructions

Build the sealed services margin: per engagement the hours at the adopted bill and cost rates, revenue capped at the contract, margin, burn, and value at risk; per role the blended rates, margin, and the bill rate the target needs; per worker billable hours, the persisted work orders' internal hours, availability from the supplied profiles, and utilisation (payload: optional window_start, window_end, profiles).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nowNo
engineNo
operationNobuild
entity_refNo
project_idYes
bundle_jsonYes
payload_jsonNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.2

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It uses the verb 'Build' but does not clarify whether this is a read-only calculation or a mutation that writes data, nor does it mention permissions, side effects, or return format. This is a significant gap for a tool with no annotation coverage.

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

Conciseness2/5

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

The description is a single dense, run-on sentence that front-loads the purpose but then enumerates many details without clear structure. It would benefit from bullet points or separate sentences to improve readability. It is verbose and not concise.

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

Completeness3/5

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

For a complex tool with no annotations and no schema descriptions, the description is fairly detailed about the computed metrics (per engagement, role, and worker) and mentions optional payload inputs. However, it omits input semantics for required parameters, side effects, and return format, leaving the agent with incomplete information to call it correctly.

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?

Schema description coverage is 0%, so the description must compensate. It only clarifies the optional payload fields (window_start, window_end, profiles) but fails to explain the required project_id, bundle_json, or other parameters like now, engine, operation, and entity_ref. The agent would have no idea what format or meaning these parameters carry.

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

Purpose4/5

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

The description clearly states it 'Builds the sealed services margin' and enumerates specific metrics computed per engagement, role, and worker, making the resource and outputs clear. However, it does not explicitly distinguish itself from sibling tools, though none directly overlap in name or purpose.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, nor any prerequisites or context for invocation. It only describes what it computes, leaving the agent to infer usage.

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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