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Glama

mcp-knowledge

by io.github.getgapup

treasury_optimizer

Read-only

Optimiseur de trésorerie — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Reference case: Alan — Trésorerie €380M post-Série F · Allocation optimale 4 instruments · Yield +145bp · +€5.5M/an. 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
horizonNo
constraintsYes
cashPositionYes

Schema Changelog

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

  1. Changed8 schema fields changed
    • addedInput schema / properties / cashPosition / properties
      Added value: +{
+  "currencies": {
+    "items": {
+      "type": "string"
+    },
+    "maxItems": 5,
+    "minItems": 1,
+    "type": "array"
+  },
+  "monthlyBurnEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "operationalReserveMonths": {
+    "maximum": 24,
+    "minimum": 1,
+    "type": "number"
+  },
+  "totalCashEur": {
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / cashPosition / required
      Added value: +[
+  "totalCashEur",
+  "operationalReserveMonths",
+  "monthlyBurnEur",
+  "currencies"
+]
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  },
+  "stage": {
+    "maxLength": 50,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "stage"
+]
    • addedInput schema / properties / constraints / properties
      Added value: +{
+  "bankPartners": {
+    "items": {
+      "type": "string"
+    },
+    "maxItems": 5,
+    "minItems": 0,
+    "type": "array"
+  },
+  "maxLockupDays": {
+    "maximum": 365,
+    "minimum": 0,
+    "type": "integer"
+  },
+  "riskAppetite": {
+    "enum": [
+      "very-conservative",
+      "conservative",
+      "moderate"
+    ],
+    "type": "string"
+  }
+}
    • addedInput schema / properties / constraints / required
      Added value: +[
+  "bankPartners"
+]
    • addedInput schema / properties / horizon / enum
      Added value: +[
+  "3mo",
+  "6mo",
+  "12mo"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "cashPosition",
+  "constraints"
+]
  2. First observed

TDQS

B3/5.0
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 it 'returns a structured, audited deliverable' and validates inputs server-side, which provides marginal value beyond annotations. No contradictions.

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 three sentences but includes a verbose reference case in French and English. It could be more concise and focused on helping the agent understand the tool's purpose and usage.

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?

The tool has complex nested parameters with no output schema. The description only says 'structured, audited deliverable' without detailing return values or how inputs map to results. This is incomplete given the tool's complexity.

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 low (20%, only async has a description). The description does not explain the meaning of the 5 parameters except via a vague reference case. It fails to compensate for the low schema coverage, leaving the agent uncertain about fields like company, cashPosition, and constraints.

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 it is an 'Optimiseur de trésorerie' that returns a structured audited deliverable, with a specific reference case. However, it does not differentiate from sibling tools like working_capital or capital_strategy, which are also financial optimization tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for CFO-level treasury optimization via a reference case, but lacks explicit guidance on when to use versus alternatives. The sibling list is large and no differentiation is provided.

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