Skip to main content
Glama

gapup-mcp

by io.github.getgapup

financial_model_3statement

Read-only

Pure-compute 3-statement financial model builder (Income Statement + Balance Sheet + Cash Flow). Feed assumptions (revenue growth, COGS%, OpEx, CapEx, working capital, tax rate, depreciation, debt schedule) → receive a full 3-5 year projection with integrated DCF valuation. Supports IFRS / US_GAAP / PRC_GAAP (中国会计准则) norms with bilingual ZH+EN labels for PRC. Modes: build (full 3-statement model) | scenario_analysis (base/bull/bear ±20% growth) | sensitivity (1 KPI × 1 input, 5-point grid). No external data needed — all computed from assumptions. ICP: VC due diligence, M&A analysts, CFO SMB, startup founders pitching investors, biotech/SaaS modeling. Returns balance_check_ok per year, DCF enterprise/equity value, and coherence warnings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesbuild = full 3-statement model | scenario_analysis = base/bull/bear | sensitivity = 1 KPI × 1 input
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.
assumptionsYesFinancial assumptions for the model
sensitivity_kpiNoKPI to observe in sensitivity mode.
sensitivity_inputNoAssumption param to vary in sensitivity mode. E.g. 'growth_rates_pct[0]' or 'cogs_pct_of_revenue'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
normsYes
statusYes
sourcesNo
warningsYes
cash_flowNo
scenariosNo
sensitivityNo
balance_sheetNo
quality_scoreYes
valuation_dcfNo
income_statementNo

TDQS

A4/5.0
Behavior4/5

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

Annotations already mark readOnly and no-open-world; description reinforces with 'Pure-compute' and 'No external data needed,' and adds output specifics such as balance_check_ok, DCF enterprise/equity value, and coherence warnings.

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

Conciseness3/5

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

Information-dense and front-loaded with the core function, but includes redundancy with schema (modes, norms) and a somewhat verbose ICP list that could be trimmed.

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?

For a complex tool with nested assumptions and an output schema, the description provides sufficient selection context: modes, accounting norms, no-external-data behavior, and output summary. Minor omission like async behavior is covered by schema.

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 coverage is 100%, so baseline is 3. Description recaps assumption categories like revenue growth and COGS% but adds no new parameter detail; '3-5 year projection' even slightly conflicts with the schema's 1-10 range.

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?

Clearly identifies the tool as a '3-statement financial model builder' with Income Statement + Balance Sheet + Cash Flow, and distinguishes it from siblings by its full-model scope and DCF outputs.

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?

States intended users (VC due diligence, M&A analysts, CFO SMB) and key constraint 'No external data needed,' making the use case clear, but does not explicitly name sibling alternatives or exclusion scenarios.

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

All gapup-mcp 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

C2.4/5.0
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.

Resources