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mcp-knowledge

by io.github.getgapup

lead_magnets

Read-only

Aimants à leads — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Spendesk — Guide trésorerie startup SaaS B2B FR/EU (2024). Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
icpYes
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.
brandYes
leadMagnetYes

Schema Changelog

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

  1. Changed7 schema fields changed
    • addedInput schema / properties / brand / properties
      Added value: +{
+  "cta": {
+    "maxLength": 150,
+    "minLength": 5,
+    "type": "string"
+  },
+  "name": {
+    "maxLength": 120,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 150,
+    "minLength": 3,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / brand / required
      Added value: +[
+  "name",
+  "sector",
+  "cta"
+]
    • addedInput schema / properties / icp / properties
      Added value: +{
+  "companySize": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "geography": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "industry": {
+    "maxLength": 150,
+    "minLength": 3,
+    "type": "string"
+  },
+  "mainPain": {
+    "maxLength": 300,
+    "minLength": 10,
+    "type": "string"
+  },
+  "persona": {
+    "maxLength": 300,
+    "minLength": 10,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / icp / required
      Added value: +[
+  "persona",
+  "industry",
+  "companySize",
+  "mainPain",
+  "geography"
+]
    • addedInput schema / properties / leadMagnet / properties
      Added value: +{
+  "budget": {
+    "maxLength": 80,
+    "minLength": 2,
+    "type": "string"
+  },
+  "format": {
+    "enum": [
+      "ebook",
+      "benchmark",
+      "calculatrice",
+      "checklist",
+      "template",
+      "guide",
+      "rapport-sectoriel",
+      "mini-cours"
+    ],
+    "type": "string"
+  },
+  "targetDownloads": {
+    "maxLength": 80,
+    "minLength": 2,
+    "type": "string"
+  },
+  "topic": {
+    "maxLength": 200,
+    "minLength": 5,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / leadMagnet / required
      Added value: +[
+  "topic",
+  "format",
+  "targetDownloads",
+  "budget"
+]
    • addedInput schema / required
      Added value: +[
+  "icp",
+  "leadMagnet",
+  "brand"
+]
  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 and openWorldHint, indicating safe, read-only behavior. The description adds that inputs are validated server-side, but does not disclose any additional behavioral traits (e.g., latency, dependency on external data, or return format). The description does not contradict 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 brief (three sentences) and front-loaded with the purpose. It avoids redundancy. However, the French phrasing may disrupt scanning for non-French agents.

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 and no output schema, the description is incomplete. It does not describe the return value format, what constitutes a 'structured, audited deliverable,' or how the inputs map to the output. The lack of output schema further burdens the description, which it fails to address.

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% (only 'async' has a description). The tool description does not explain the meaning or usage of the three required nested objects (icp, brand, leadMagnet) beyond a vague 'send the documented case fields.' The reference case gives an example but lacks general parameter semantics.

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 states it returns a structured, audited deliverable for lead magnets targeting C-suite (CMO). The verb 'returns' is clear, and the reference case adds context. However, it does not explicitly differentiate from sibling tools, and the French language may reduce clarity for English-only agents.

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?

The description provides no guidance on when to use this tool versus alternatives. It only mentions that inputs are validated server-side, but does not specify use cases, prerequisites, or when not to use. With many sibling marketing tools, the lack of usage guidance is a significant gap.

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