gapup-mcp

gl_reconciler

Read-only

GL Reconciler — Réconciliation grand livre — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Identify the root causes of the GL breaks in 's ledger for — cluster them and rank by materiality. · For Q close: which accounts have unreconciled items over €? Provide a sign-off routing and resolution plan. · Run an automated GL reconciliation for — AR/AP/intercompany entries — flag open items, suggest journal entries. · What are the top 5 systemic control weaknesses causing recurring GL breaks at ? Recommend preventive controls. · Generate a month-end close reconciliation report for — breaks by account type, aging analysis, sign-off assignments. Reference case: Acme SaaS Q4 2026 — 47 breaks GL, €1.4M variance non postée. 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
`ledgerContext`	Yes

TDQS

C2.7/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that inputs are validated server-side and that a structured deliverable is returned, which is consistent with read-only behavior. However, it does not disclose further behavioral traits like rate limits, required permissions, or how results can be polled (despite the async parameter).

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 long and unstructured, mixing French and English, marketing language, and a reference case ('Acme SaaS Q4 2026...'). It could be condensed into a clear single sentence about the tool's function, followed by parameter explanations.

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?

The description covers the general purpose and gives concrete examples of what the tool can answer, which is helpful given the lack of output schema. However, it does not specify the output structure or how to correctly fill the nested parameters (e.g., 'entity' fields), leaving gaps for an AI agent.

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 has 4 parameters with only 25% description coverage (async has a description). The description does not explain the 'entity', 'focus', or 'ledgerContext' parameters or how they map to the example queries. With such low schema coverage, the description should compensate but fails to do so.

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 states the tool performs GL reconciliation and returns a structured deliverable. It lists example user queries, such as identifying root causes of GL breaks and generating month-end close reports, making the purpose clear. However, it does not explicitly state that it is a read-only analysis tool, relying on annotations for that.

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 example queries but gives no guidance on when to use this tool versus its many siblings (e.g., financial_model_3statement, audit_pre_flight). No explicit when-to-use or when-not-to-use instructions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All gapup-mcp tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?