Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

pitch_deck_storyline

Read-onlyIdempotent

Build a complete investor pitch-deck storyline for a company. Returns an 8-20 slide narrative tailored to the target audience (seed-vc / series-a-vc / growth-vc / strategic / bank / grant) — each slide carrying a title, key points, a speaker note and a visual hint — plus a Q&A bank of 10-15 likely board questions and traps to avoid. Output is deck JSON ready to export to Google Slides, Notion or Pitch.com. When to use this tool: the user is preparing a fundraise, a board meeting, or an investor presentation. Inputs: the company profile and the target audience type. Delivered by Sarah, the AI Fundraising lead of the Gapup portfolio.

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
audienceYesTarget audience — adapts tone + emphasis + Q&A bank
keyFactsYesHard facts to weave into the deck (traction numbers, milestones, awards)
slideCountYes12 = standard VC deck, 15 = bank-friendly with annexes, 20 = growth/strategic

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kpisNo3-5 headline KPI bubbles surfaced from keyFacts
slidesYes8-20 slide objects ready to export to Google Slides / Notion / Pitch.com
qaBanksYes10-15 anticipated investor questions with recommended answers
recommendationsNoFundraising preparation actions
executiveSummaryYesOne-paragraph elevator pitch distilled from the deck

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description does not contradict these. It adds minimal behavioral context beyond noting the async capability (via the async parameter description) and that the output is 'deck JSON ready to export.' No mention of rate limits, authentication, or side effects, but the annotations cover the key safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is around 100 words, well-structured, and front-loaded with the core action. Every sentence adds value: purpose, output summary, usage guidance, inputs, and even a touch of personality ('Delivered by Sarah'). No redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (5 parameters, nested objects, enums, and an existing output schema), the description covers all essential aspects: what the tool does, when to use it, what to provide, and what to expect as output. The addition of Q&A bank and visual hints further enriches completeness. The AI has enough context to decide whether to invoke this tool.

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

Parameters4/5

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

Schema coverage is 80%, so the schema already documents most parameters. The description adds valuable context: for slideCount, it provides example mappings ('12 = standard VC deck, 15 = bank-friendly...'); for keyFacts, it says 'Hard facts to weave into the deck (traction numbers, milestones, awards)'; and it summarizes inputs as 'company profile and target audience type.' This enhances understanding beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Build a complete investor pitch-deck storyline for a company.' It specifies the output format (8-20 slides with title, key points, speaker notes, visual hints, plus Q&A bank). The verb 'build' and resource 'pitch-deck storyline' are specific, and the tool is clearly differentiated from siblings by its focus on fundraising presentations.

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

Usage Guidelines4/5

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

The description provides explicit guidance: 'When to use this tool: the user is preparing a fundraise, a board meeting, or an investor presentation.' It does not explicitly mention when not to use it or alternatives, but the context is clear enough for an AI to infer appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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