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Glama

mcp-knowledge

by io.github.getgapup

procurement_spend_optim

Read-only

Optimisation des achats / Spend strategy — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Tech SaaS €60M ARR — 200 fournisseurs analysés · 20 leviers chiffrés · -€2.4M opex/an target. 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.
focusNo
companyYes
topSuppliersYes
spendCategoriesYes

Schema Changelog

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

  1. Changed10 schema fields changed
    • addedInput schema / properties / company / properties
      Added value: +{
+  "fte": {
+    "minimum": 1,
+    "type": "integer"
+  },
+  "name": {
+    "type": "string"
+  },
+  "revenueEur": {
+    "type": "number"
+  },
+  "sector": {
+    "type": "string"
+  },
+  "totalAnnualSpendEur": {
+    "type": "number"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "fte",
+  "revenueEur",
+  "totalAnnualSpendEur"
+]
    • addedInput schema / properties / focus / maxLength
      Added value: +400
    • addedInput schema / properties / spendCategories / items
      Added value: +{
+  "properties": {
+    "annualSpendEur": {
+      "type": "number"
+    },
+    "category": {
+      "type": "string"
+    },
+    "description": {
+      "maxLength": 300,
+      "type": "string"
+    },
+    "supplierCount": {
+      "type": "integer"
+    }
+  },
+  "required": [
+    "category",
+    "annualSpendEur",
+    "supplierCount"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / spendCategories / maxItems
      Added value: +20
    • addedInput schema / properties / spendCategories / minItems
      Added value: +5
    • addedInput schema / properties / topSuppliers / items
      Added value: +{
+  "properties": {
+    "annualSpendEur": {
+      "type": "number"
+    },
+    "category": {
+      "type": "string"
+    },
+    "contractEnd": {
+      "type": "string"
+    },
+    "name": {
+      "type": "string"
+    },
+    "notes": {
+      "maxLength": 300,
+      "type": "string"
+    }
+  },
+  "required": [
+    "name",
+    "category",
+    "annualSpendEur"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / topSuppliers / maxItems
      Added value: +50
    • addedInput schema / properties / topSuppliers / minItems
      Added value: +10
    • addedInput schema / required
      Added value: +[
+  "company",
+  "spendCategories",
+  "topSuppliers"
+]
  2. First observed

TDQS

C2.9/5.0
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 returns an audited deliverable, which is consistent but not extensive. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two sentences plus a case example) and front-loaded with purpose. The reference case adds some value but is not essential; overall it is efficient.

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 objects, 5 parameters, no output schema), the description is insufficient. It lacks details on return format, how the deliverable is structured, and when this specific tool is preferred over siblings.

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 coverage is only 20%, and the description provides no elaboration on parameters beyond 'send documented case fields.' It fails to compensate for the missing schema descriptions, leaving agents without understanding the meaning of fields like 'focus' or nested objects.

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 states the tool's purpose: procurement spend optimization targeting CFO-level expertise. It mentions a reference case and return of a structured deliverable, distinguishing it from sibling tools like procurement_six_sigma_waste_hunter.

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. It implies use for CFO spend analysis but does not compare to other procurement tools or state prerequisites/exclusions.

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