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mcp-knowledge

by io.github.getgapup

process_mining

Read-only

Mining des process — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Gapup Hub — 4 process · €320k gaspillage identifié · 3 quick wins · 5 automations. 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.
objectivesYes
companyNameYes
mainSystemsYes
topProcessesYes
employeeCountYes
revenueLostEstimateEurNo

Schema Changelog

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

  1. Changed10 schema fields changed
    • addedInput schema / properties / mainSystems / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / mainSystems / maxItems
      Added value: +8
    • addedInput schema / properties / mainSystems / minItems
      Added value: +1
    • addedInput schema / properties / objectives / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / objectives / maxItems
      Added value: +4
    • addedInput schema / properties / objectives / minItems
      Added value: +1
    • addedInput schema / properties / topProcesses / items
      Added value: +{
+  "properties": {
+    "currentPain": {
+      "type": "string"
+    },
+    "department": {
+      "type": "string"
+    },
+    "estimatedDurationDays": {
+      "type": "number"
+    },
+    "involvedRoles": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "name": {
+      "type": "string"
+    },
+    "stepCount": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "name",
+    "department",
+    "estimatedDurationDays",
+    "stepCount",
+    "involvedRoles",
+    "currentPain"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / topProcesses / maxItems
      Added value: +6
    • addedInput schema / properties / topProcesses / minItems
      Added value: +2
    • addedInput schema / required
      Added value: +[
+  "companyName",
+  "employeeCount",
+  "topProcesses",
+  "objectives",
+  "mainSystems"
+]
  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. The description adds that inputs are validated server-side and that a structured, audited deliverable is returned, but does not elaborate on behavioral traits like rate limits, authentication needs, or processing time. No annotation contradiction.

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 relatively short but mixes languages (French and English) and includes a reference case that may not be universally understood. While not verbose, it could be more structured and focused on essential information.

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 no output schema and minimal parameter descriptions, the description lacks completeness. It does not explain the return format, expected result structure, or how to interpret the 'structured, audited deliverable.' The context of the reference case is insufficient for a comprehensive understanding.

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?

With schema description coverage at only 14%, the description fails to explain what each parameter means or how to use them. It merely says 'send the documented case fields' without clarifying which fields are required or their semantics. This provides virtually no value beyond the bare property names.

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 'Mining des process' and mentions returns a structured deliverable, but the verb is not explicitly stated (e.g., 'analyze' or 'mine' processes). The reference case hints at waste identification and automations, but purpose remains vague and does not distinguish from sibling 'process_mapping'.

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 direct guidance on when to use this tool versus alternatives is provided. The description mentions 'Gapup agent-payable C-suite expertise (COO)' implying executive use case but does not give context-specific conditions or exclusions. Sibling tools with similar purposes exist, but no differentiation is offered.

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

C2.8/5.0
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