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mcp-knowledge

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

A4.1/5.0
Behavior4/5

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

Annotations provide readOnlyHint=true, which the description supports by calling it 'pure-compute' with no external data needed. It discloses outputs (balance_check_ok, DCF values, coherence warnings) and mentions bilingual labels for PRC_GAAP. No contradictions. Could add more on async behavior (covered in schema) or limitations.

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 long but well-structured, front-loading the primary function and then listing details. Every sentence adds value (modes, ICP, outputs). Could be slightly trimmed but overall effective.

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?

Given the complexity (3-statement model, multiple modes, accounting standards, bilingual labels), the description covers key aspects: input assumptions, modes, ICP, outputs. Output schema exists so return values are not needed. Minor gap: no mention of async poll but schema covers it.

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 100%, so every parameter has a detailed explanation. The description adds a high-level summary of assumptions (revenue growth, COGS%, etc.) but does not provide additional meaning beyond the schema. 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 clearly states it builds a 3-statement financial model (Income Statement, Balance Sheet, Cash Flow) with DCF valuation. It distinguishes from siblings by specifying 'no external data needed' and 'pure-compute', and lists specific modes (build, scenario_analysis, sensitivity) and supported accounting standards (IFRS, US_GAAP, PRC_GAAP). This sets it apart from other financial tools like capital_strategy or working_capital.

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 says 'Feed assumptions → receive a full 3-5 year projection' and explains the three modes and their purposes. It provides target ICP (VC due diligence, M&A analysts, CFO SMB, startup founders). It does not explicitly state when not to use it or name alternatives, but the mode descriptions and context against siblings imply appropriate use cases.

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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