Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

audit_pre_flight

Read-only

Pré-audit comptable — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Spendesk — Pré-audit commissaire · Readiness 74/100 · 4 findings critiques · Checklist 18 docs. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf 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.
auditYes
companyYes
systemsYes
financialsYes
knownIssuesYes

Schema Changelog

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

  1. Changed13 schema fields changed
    • addedInput schema / properties / audit / properties
      Added value: +{
+  "auditor": {
+    "maxLength": 100,
+    "type": "string"
+  },
+  "scheduledDate": {
+    "type": "string"
+  },
+  "scope": {
+    "maxLength": 300,
+    "minLength": 5,
+    "type": "string"
+  },
+  "type": {
+    "enum": [
+      "statutory",
+      "internal",
+      "due-diligence",
+      "ipo-readiness",
+      "sox"
+    ],
+    "type": "string"
+  }
+}
    • addedInput schema / properties / audit / required
      Added value: +[
+  "type"
+]
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "stage": {
+    "enum": [
+      "pre-seed",
+      "seed",
+      "serie-a",
+      "serie-b",
+      "serie-c",
+      "serie-d-plus",
+      "profitable-sme",
+      "enterprise"
+    ],
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "stage"
+]
    • addedInput schema / properties / financials / properties
      Added value: +{
+  "annualRevenueEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "closingDateLastYear": {
+    "type": "string"
+  },
+  "totalAssetsEur": {
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / financials / required
      Added value: +[
+  "annualRevenueEur",
+  "totalAssetsEur"
+]
    • addedInput schema / properties / knownIssues / items
      Added value: +{
+  "maxLength": 300,
+  "minLength": 5,
+  "type": "string"
+}
    • addedInput schema / properties / knownIssues / maxItems
      Added value: +6
    • addedInput schema / properties / knownIssues / minItems
      Added value: +0
    • addedInput schema / properties / systems / items
      Added value: +{
+  "maxLength": 100,
+  "type": "string"
+}
    • addedInput schema / properties / systems / maxItems
      Added value: +8
    • addedInput schema / properties / systems / minItems
      Added value: +0
    • addedInput schema / required
      Added value: +[
+  "company",
+  "audit",
+  "financials",
+  "knownIssues",
+  "systems"
+]
  2. First observed

TDQS

C2.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe, read-only behavior. The description adds that inputs are validated server-side and returns a deliverable, which is consistent but does not significantly expand on behavioral traits beyond what annotations and schema (async handling) provide.

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 short (3 sentences) but includes a specific reference case that may be irrelevant for general usage. It lacks structured formatting (e.g., bullet points) and contains jargon (French terms, 'Gapup agent-payable'), which reduces clarity. Not overly long, but not highly concise or well-structured.

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 (nested parameters, no output schema, low schema coverage), the description is insufficient. It does not specify the return format, readiness scoring, or how to interpret findings. Users cannot determine what the deliverable contains or how to handle results, leaving significant gaps for effective usage.

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?

Schema description coverage is low (17%), with only the 'async' parameter described. The tool description does not explain any parameters, merely stating to send 'documented case fields.' This leaves most parameters (nested objects like company, audit, financials) without semantic clarification, failing to compensate for the schema's lack of descriptions.

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 states the tool performs a pre-audit (Pré-audit comptable) and returns a structured deliverable, but the phrasing mixes French and jargon without explicitly stating the output format (e.g., readiness score, findings). The reference case provides an example but lacks generalization, making it moderately clear but not fully transparent.

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 guidance is provided on when to use this tool versus alternatives. There is no mention of appropriate use cases, prerequisites, or exclusions. The sibling list contains many tools but no differentiation is made.

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

All mcp-knowledge toolsInspect
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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources