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mcp-knowledge

by io.github.getgapup

qa_pre_flight

Read-only

Préparation Q&A investisseurs — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Agicap Série C €70M — 30 Q&A stratégiques · 8 questions pièges · Plan de préparation 21 jours. 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.
roundYes
companyYes
founderContextYes

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: +{
+  "currentArrEur": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "founded": {
+    "maximum": 2030,
+    "minimum": 2000,
+    "type": "number"
+  },
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  },
+  "teamSize": {
+    "maximum": 10000,
+    "minimum": 1,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector",
+  "currentArrEur",
+  "teamSize",
+  "founded"
+]
    • addedInput schema / properties / founderContext / properties
      Added value: +{
+  "knownWeaknesses": {
+    "items": {
+      "maxLength": 200,
+      "type": "string"
+    },
+    "maxItems": 5,
+    "minItems": 0,
+    "type": "array"
+  },
+  "pitch": {
+    "maxLength": 1000,
+    "minLength": 30,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / founderContext / required
      Added value: +[
+  "pitch",
+  "knownWeaknesses"
+]
    • addedInput schema / properties / round / properties
      Added value: +{
+  "leadInvestorProfile": {
+    "maxLength": 200,
+    "minLength": 5,
+    "type": "string"
+  },
+  "stage": {
+    "enum": [
+      "pre-seed",
+      "seed",
+      "serie-a",
+      "serie-b",
+      "serie-c",
+      "growth"
+    ],
+    "type": "string"
+  },
+  "targetEur": {
+    "minimum": 0,
+    "type": "number"
+  }
+}
    • addedInput schema / properties / round / required
      Added value: +[
+  "stage",
+  "targetEur"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "round",
+  "founderContext"
+]
  2. First observed

TDQS

C2.9/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, which are not contradicted. The description adds that inputs are validated server-side and returns a structured audited deliverable. However, it does not disclose whether this tool modifies any state (despite readOnlyHint), what happens on validation failure, or any rate limits. With annotations covering the safety profile, the description provides modest additional context.

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 reference case) and front-loads the core purpose. However, it could be more structured (e.g., bullet points), and the reference case is useful but not essential. No wasted words.

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?

With 4 parameters including deeply nested objects, no output schema, and a brief description, the tool is under-documented. The description does not explain the deliverable format (beyond 'structured, audited'), how to interpret results, or what triggers validations. For a tool likely generating significant content, this is insufficient.

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 adds no information about individual parameters beyond 'send the documented case fields'. The nested objects (company, round, founderContext) and their many properties are left entirely to the schema. The description does not explain how to fill in fields like 'pitch', 'knownWeaknesses', or 'leadInvestorProfile'.

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 prepares investor Q&A for fundraising, returning a structured deliverable. It references a concrete case (Agicap Série C) to illustrate scope. However, the verb 'Préparation' is somewhat vague, and it doesn't distinguish well from many sibling tools like 'pitch_deck_storyline' or 'investor_shortlist'.

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 when-to-use or when-not-to-use guidance is provided. The description mentions fundraising context, but given dozens of related siblings (e.g., 'pitch_deck_storyline', 'investor_shortlist', 'ma_deal_screener'), an agent would have no basis to choose this tool over alternatives.

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