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Glama

Forge Engine

by app.forgeengine

get_balance

Read-only

Read Balance Lab: stat tables, boards with EVALUATED values, scenarios.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
boardNoOne board (omit = all)
project_idNo
include_rowsNofalse = schema only

Schema Changelog

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

  1. Changed5 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • changedInput schema / properties / board / description
      Previous value: -"Optional — a board name or id to evaluate just that board (omit = all boards)."New value: +"One board (omit = all)"
    • changedInput schema / properties / include_rows / description
      Previous value: -"Include table rows (default true; first 200 per table). Pass false for a schema-only view."New value: +"false = schema only"
    • removedInput schema / properties / project_id / description
      Removed value: -"Project id (from list_projects) to act on; omit = the connector URL's project."
    • removedInput schema / required
      Removed value: -[]
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

The description aligns with the readOnlyHint annotation and adds slight context about returning 'EVALUATED values' and covering stat tables, boards, and scenarios. However, it does not explain what 'EVALUATED' means, how board omission behaves, or the return structure, leaving gaps.

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?

A single, front-loaded sentence with no filler. It efficiently conveys the resource and verb, though 'EVALUATED' and 'scenarios' are jargon-y, but that doesn't detract from conciseness.

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

Completeness2/5

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

The tool has no output schema and a sparse description. It doesn't explain the domain terms (Balance Lab, EVALUATED values, scenarios), the meaning of including or omitting board, or what 'schema only' means for include_rows. Despite the readOnly annotation, an agent would need more context to invoke correctly.

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 schema already describes board and include_rows, but project_id is undocumented. The description provides no parameter semantics, so it doesn't help disambiguate the undocumented parameter or add meaning beyond the schema.

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 uses the specific verb 'Read' and identifies the Balance Lab resource with sub-resources (stat tables, boards, scenarios). This clearly distinguishes it from sibling tools like propose_balance_board and propose_balance_table, which are proposal/write operations.

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

Usage Guidelines3/5

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

The description implies a read-only usage for retrieving Balance Lab data, but provides no explicit guidance on when to choose this over alternatives or any exclusion criteria. There's no mention of alternatives or when not to use it, so the agent must infer based on the 'Read' verb.

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

A3.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with explicit distinctions between direct actions and proposals via Inbox. The verbs and object types (system, milestone, screen, element, balance) are unique enough that no two tools appear to do the same thing.

Naming Consistency4/5

Most tool names follow a consistent verb_noun snake_case pattern (get_system, propose_screen, update_element). Minor deviations like 'dedupe', 'search', 'next_task', and 'reorder' are single words or non-verb but remain readable and stylistically compatible.

Tool Count1/5

With 54 tools, this server vastly exceeds the typical MCP scope, hitting the 'extreme mismatch' threshold. Even for a complex domain, the sheer number will overwhelm agents and degrade selection performance.

Completeness5/5

The tool surface is remarkably complete, covering full lifecycle operations for all major entities, plus import, design generation, drift detection, status reporting, inbox handling, and rejection workflows. No obvious dead ends or missing operations for the stated purpose.

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