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mcp-knowledge

by io.github.getgapup

cap_table_strategist

Read-only

Stratège du cap table — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Aleph AI Series B — modèle dilution multi-rounds + simulations secondaires + hygiène equity · 5 scenarios. 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
plannedRoundsYes
currentCapTableYes
founderObjectivesYes

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the description's mention of returning a structured deliverable adds moderate value. However, it does not disclose other behavioral aspects like authentication needs, rate limits, or what happens with complex inputs.

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 moderately concise with four sentences, including a reference case that adds context without excessive verbosity. It is front-loaded with the main purpose but could be slightly shorter without the case study.

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 (6 parameters, nested objects, no output schema), the description is insufficient. It does not explain the deliverable's structure, how to interpret results, or provide usage examples beyond the reference case. Critical details for effective use are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is very low (17%), and the description adds no individual parameter explanations beyond 'send the documented case fields.' It fails to compensate for the lack of schema documentation, leaving parameters like 'focus' and nested objects underspecified.

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 identifies the tool as a cap table strategist for C-suite fundraising expertise. It mentions returning a structured, audited deliverable and provides a reference case. However, it does not explicitly distinguish it from sibling tools like 'capital_strategy', and the French language may limit accessibility for some agents.

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 fundraising cap table strategies and mentions server-side input validation, but it lacks explicit guidance on when to use versus alternatives or when not to use it. No exclusions or alternative tool references are 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.

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