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mcp-knowledge

by io.github.getgapup

vendor_management

Read-only

Gestion des fournisseurs — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Qonto (12 fournisseurs · €2.4M/an) — €290k économies identifiées · 4 renegociations prioritaires. 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.
companyYes
vendorsYes
objectivesYes

Schema Changelog

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

  1. Changed7 schema fields changed
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "totalVendorSpendEur": {
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "totalVendorSpendEur"
+]
    • addedInput schema / properties / objectives / properties
      Added value: +{
+  "complianceFocus": {
+    "type": "boolean"
+  },
+  "consolidationTarget": {
+    "type": "boolean"
+  },
+  "targetSavingsPct": {
+    "maximum": 50,
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / vendors / items
      Added value: +{
+  "properties": {
+    "annualSpendEur": {
+      "minimum": 0,
+      "type": "number"
+    },
+    "category": {
+      "maxLength": 60,
+      "minLength": 2,
+      "type": "string"
+    },
+    "contractEndDate": {
+      "maxLength": 20,
+      "type": "string"
+    },
+    "criticalityLevel": {
+      "enum": [
+        "critical",
+        "important",
+        "standard"
+      ],
+      "type": "string"
+    },
+    "name": {
+      "maxLength": 100,
+      "minLength": 2,
+      "type": "string"
+    },
+    "satisfactionScore": {
+      "maximum": 10,
+      "minimum": 0,
+      "type": "number"
+    },
+    "singleSource": {
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "name",
+    "category",
+    "annualSpendEur",
+    "criticalityLevel"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / vendors / maxItems
      Added value: +20
    • addedInput schema / properties / vendors / minItems
      Added value: +3
    • addedInput schema / required
      Added value: +[
+  "company",
+  "vendors",
+  "objectives"
+]
  2. First observed

TDQS

C2.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the async option and server-side validation provide some additional context. However, the description does not elaborate on what the deliverable contains, how long the analysis takes, or any specific behavioral traits beyond what annotations 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?

Description is concise at four sentences but adds a lengthy reference case with specific numbers that may not be generalizable and could confuse non-French readers. The structure front-loads a vague purpose but wastes words on a case example instead of core functionality.

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 complexity of nested objects, no output schema, and 200+ siblings, the description is insufficient. It does not specify the deliverable format, what the tool returns, or how to differentiate from sibling tools like procurement_spend_optim or vendor_risk_assessor.

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%, yet the description compensates poorly. It merely says 'send the documented case fields' without explaining any of the three required parameters (company, vendors, objectives) or their nested properties. The async parameter is documented in the schema but not in the description.

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?

Description states it's for vendor management and mentions a reference case showing savings identification, but lacks a clear verb specifying the exact action (e.g., analyze, audit, optimize). With 200+ siblings including vendor_esg_blacklist_monitor and vendor_risk_assessor, the description does not clearly distinguish this tool's core purpose.

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 mention of 'C-suite expertise (COO)' implies executive-level use but does not specify criteria for choosing this over similar vendor tools. There is no 'when not to use' or comparison to siblings.

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