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Glama

mcp-knowledge

by io.github.getgapup

investor_list

Read-only

Liste d'investisseurs + warm intros — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Agicap Série D — 25 VCs matchés · Tier A: Balderton/Accel/Partech · Warm intro path chaque investisseur. 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
existingInvestorsNo

Schema Changelog

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

  1. Changed8 schema fields changed
    • addedInput schema / properties / company / properties
      Added value: +{
+  "name": {
+    "maxLength": 200,
+    "minLength": 2,
+    "type": "string"
+  },
+  "sector": {
+    "maxLength": 100,
+    "minLength": 2,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / company / required
      Added value: +[
+  "name",
+  "sector"
+]
    • addedInput schema / properties / existingInvestors / items
      Added value: +{
+  "maxLength": 100,
+  "type": "string"
+}
    • addedInput schema / properties / existingInvestors / maxItems
      Added value: +5
    • addedInput schema / properties / existingInvestors / minItems
      Added value: +0
    • addedInput schema / properties / round / properties
      Added value: +{
+  "arrCurrent": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "geography": {
+    "items": {
+      "maxLength": 50,
+      "type": "string"
+    },
+    "maxItems": 5,
+    "minItems": 1,
+    "type": "array"
+  },
+  "stage": {
+    "enum": [
+      "pre-seed",
+      "seed",
+      "series-a",
+      "series-b",
+      "series-c",
+      "growth",
+      "pre-ipo"
+    ],
+    "type": "string"
+  },
+  "ticketSizeMax": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "ticketSizeMin": {
+    "minimum": 0,
+    "type": "number"
+  },
+  "useCase": {
+    "maxLength": 300,
+    "minLength": 10,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / round / required
      Added value: +[
+  "stage",
+  "ticketSizeMin",
+  "ticketSizeMax",
+  "geography",
+  "useCase"
+]
    • addedInput schema / required
      Added value: +[
+  "company",
+  "round"
+]
  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, so the agent knows it's read-only. The description adds that inputs are validated server-side and returns a structured deliverable, but does not disclose potential async behavior or output format details. Some value added, but not rich.

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 relatively short at four sentences, with the core purpose front-loaded. The reference case adds some value without excessive bloat. It could be slightly more streamlined, but overall concise.

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 low parameter coverage, the description is incomplete. It fails to explain the output format, the meaning of parameters, or the async option. The tool's behavior is not fully described, leaving gaps for the agent.

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%, and the description does not compensate. Only the 'async' parameter is mentioned indirectly; the nested parameters like 'round.stage' and 'company.name' are not explained. The description says to send 'documented case fields' but does not clarify what each field means.

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 returns a list of investors with warm introductions for fundraising, including a reference case. However, it does not differentiate from similar sibling tools like 'investor_shortlist', so it loses a point for not distinguishing.

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 guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, when not to use it, or comparison with other tools, leaving the agent without usage context.

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