gapup-mcp

budget_variance_ai

Read-only

Analyse d'écart budgétaire — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Explain the key drivers of the budget vs actual variance for in — what are the top 10 narrative explanations? · Which cost categories drove the budget overrun for in , and what corrective actions should management take? · Revise the Q4 forecast based on observed Q3 variances for — give me 3 scenarios (base, optimistic, conservative). · Prepare a board-ready budget variance memo for — , budget €M vs actual €M, with management actions. · What are the quick wins to reduce budget overspend for by end of quarter without impacting growth targets? Reference case: Doctolib Q3 2026 — budget €38.5M vs actual €41.2M (+7.0%) — cloud + headcount + deals timing. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`focus`	No
`entity`	Yes
`budgetContext`	Yes

TDQS

B3/5.0

Behavior4/5

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

Beyond the annotations (readOnlyHint, openWorldHint), the description adds that inputs are validated server-side and that it returns a structured, audited deliverable. This provides useful behavioral context without contradicting annotations.

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 moderately concise but includes a verbose list of example queries and mixes French and English. It could be more structured by separating core function from examples.

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?

Given the tool's complexity (4 parameters, nested objects, no output schema) and many siblings, the description lacks details on return format, error handling, async behavior documentation (only in schema), and how to interpret results. It feels incomplete.

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 only 25% (only async parameter documented). The description does not explain the meaning of entity or budgetContext parameters, despite their nested structure and required fields. Users must infer from the example queries.

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 identifies the tool as a budget variance analysis tool that returns a structured deliverable, with example queries specifying its capabilities. However, it does not sharply distinguish it from similar finance tools like earnings_reviewer or financial_model_3statement.

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?

No explicit guidance on when to use this tool versus alternatives. The description provides example queries but lacks context about prerequisites, limitations, or comparisons with sibling tools.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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