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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

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description doesn't bear the full safety burden. The description adds useful context about the output structure and export targets (Google Slides, Notion, Pitch.com), but it does not disclose potential caveats like latency, async behavior, or failure modes. It also includes an irrelevant 'Delivered by Sarah' persona note, which adds noise rather than behavioral insight.

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 and front-loaded, stating the main action and output first, then usage context. The 'When to use' phrase is helpful, but the sentence 'Delivered by Sarah, the AI Fundraising lead of the Gapup portfolio' is unnecessary fluff that does not help the agent select or invoke the tool. Otherwise, every sentence earns its place.

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

Completeness4/5

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

The tool has moderate complexity (nested objects, 5 params, output schema), and the description covers its purpose, when to use it, inputs, and output format. Since an output schema exists, detailed return-value explanation is unnecessary. The description is missing mention of the async option, but the schema documents it, so the context is complete enough for an agent to use the tool correctly.

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

Parameters3/5

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

Schema description coverage is high (80%), with detailed descriptions for audience ('adapts tone + emphasis + Q&A bank'), slideCount ('12 = standard VC deck...'), and keyFacts. The description merely restates that inputs are 'the company profile and the target audience type,' adding no new meaning beyond the schema. It does not clarify the 'company' nested object parameters or the 'async' flag, but the schema already covers these, so the baseline 3 is appropriate.

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 opens with a specific verb+resource: 'Build a complete investor pitch-deck storyline for a company.' It further details the slide narrative (8-20 slides, each with title, key points, speaker note, visual hint) and the Q&A bank, making the tool's function unambiguous. While sibling tools like capital_strategy or investor_shortlist are adjacent, none claim to produce a full pitch-deck storyline, so this description clearly distinguishes the tool.

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 explicitly states the trigger condition: 'When to use this tool: the user is preparing a fundraise, a board meeting, or an investor presentation.' This gives clear context for when the tool is appropriate. However, it does not mention when not to use it or suggest alternative tools, so it falls short of a full 5.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.