The cvm-mcp server provides financial data from the CVM's Open Data Portal, enabling LLMs to consult financial statements of publicly traded companies. You can:
Search for companies by name, CNPJ, or CVM code before conducting any analysis.
Analyze companies with a standard 5-year financial analysis covering metrics like Net Revenue, EBIT, estimated EBITDA, Net Income, Net Margin, EBITDA Margin, ROE, Current Liquidity, Gross Debt, Cash, Net Debt, and Net Debt/EBITDA.
Obtain financial indicators for a specified range of years, with options for consolidated or individual statements.
Retrieve raw financial statements from specific statements (e.g., BPA, BPP, DRE, DFC_MD, DFC_MI, DVA, DMPL, DRA) for a given year, including account codes, descriptions, and values in R$ millions, with consolidated or individual statement options.
Allows querying financial data from the Brazilian Securities and Exchange Commission (CVM) through an MCP server that retrieves company financial statements, indicators, and raw accounting data.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@cvm-mcp analise a Petrobras"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server that fetches and processes financial data directly from the CVM Open Data Portal (dados.cvm.gov.br), without relying on third parties. It exposes tools for an LLM client (Claude Desktop, Claude Code, etc.) to query the financial statements published by publicly traded companies.
The default view is quarterly: the tools start from the last quarter the company published and return the previous 12 months, quarter by quarter, with the accounting accounts as published by the CVM — without calculated indicators.
Tool | When to use |
| Resolve name/CNPJ/CVM code before any analysis |
| Generic request ("analyze company X") — DRE for the last 4 quarters |
| Other statement (balance sheet, cash flow) or more than 4 quarters |
| When the user explicitly asks for the closed annual period (DFP) |
All monetary values are normalized to R$ millions. See Limitations below — the AI always receives warnings when a data point is derived or could not be obtained.
Related MCP server: FinancialReports MCP Server
The CVM's ITR publishes only Q1, Q2, and Q3, and already brings each quarter isolated in addition to the year-to-date accumulations — so cutting a quarter is filtering by period, not subtracting.
Q4 does not exist in the ITR. It is derived as exercício completo (DFP) − acumulado até o 3T (ITR), matched account by account by the accounting code. Every such period is marked with derivado: true in the response, along with an explicit warning.
Two consequences worth understanding:
Stock accounts are never derived. BPA and BPP are balances on a date, so the year-end closing is already the Q4 value. Only flow accounts (DRE, DFC, DVA, DRA, DMPL) undergo subtraction.
The "last quarter" is per company, not global. Non-calendar fiscal years close in other months — Camil, for example, has quarters Mar–May, Jun–Aug, Sep–Nov, and Dec–Feb. The server resolves this by each company's dates, and the label (2T26) is always accompanied by inicio and fim.
Requires Python 3.10+. Works the same on Windows, macOS, and Linux.
pipx install .Runs from any folder afterwards, as the command cvm-mcp.
python -m venv .venv
# Windows (PowerShell)
.venv\Scripts\Activate.ps1
# macOS / Linux
source .venv/bin/activate
pip install -e .pip install -r requirements.txt # ou: pip install mcp[cli] httpx pandas platformdirs
python -m cvm_mcpEdit claude_desktop_config.json (Windows: %APPDATA%\Claude\claude_desktop_config.json; macOS: ~/Library/Application Support/Claude/claude_desktop_config.json) and add:
{
"mcpServers": {
"cvm": {
"command": "cvm-mcp"
}
}
}If you prefer not to install with pipx (Option 3), use:
{
"mcpServers": {
"cvm": {
"command": "python",
"args": ["-m", "cvm_mcp"]
}
}
}The files downloaded from the CVM (registry + annual ZIPs of ITR and DFP) are cached locally to avoid re-downloading on every query — the check uses ETag/Last-Modified, so updates on the CVM portal are detected automatically.
Worth sizing: the packages come compressed (~20–30 MB per year), but are extracted for use, and each year-type takes up a few hundred MB on disk. The quarterly view typically touches two years of ITR plus one year of DFP.
Default location (via platformdirs, without OS hardcoding):
Windows: %LOCALAPPDATA%\cvm-mcp
macOS: ~/Library/Caches/cvm-mcp
Linux: ~/.cache/cvm-mcp
To use another directory (e.g., restricted environments/CI), set CVM_MCP_CACHE_DIR before running the server.
Q4 is derived, not published. The CVM does not disclose the 4th quarter separately; the value comes from ano cheio − acumulado 9M. It matches the fiscal year by construction, but it is not a number the company reported.
No calculated indicators. This version returns published accounting accounts, not margins, ROE, or EBITDA. The indicator code remains in the repository (indicators.py, accounts.py), not linked to any tool, to be readapted to the quarterly base later.
No market data: the CVM does not publish stock prices, market value, or multiples (P/E, EV/EBITDA). Requests of this type are outside the scope of this source.
Stock accounts do not add up. BPA and BPP are balances: adding up the 4 quarters of equity produces nothing meaningful. Only flow accounts can be accumulated over 12 months.
Not every company has 4 quarters (recent IPO, suspension, registration cancellation, late ITR filing). The window returns the periods that exist, without filling gaps with zero.
Financial sector companies use a different DRE chart of accounts, so the accounting codes are not comparable line by line with those of non-financial companies.
python -m venv .venv
.venv\Scripts\Activate.ps1 # ou source .venv/bin/activate
pip install -e .
python -m cvm_mcp # roda o servidor via stdioProject structure:
src/cvm_mcp/
config.py # constantes e diretório de cache (cross-platform)
cache.py # download HTTP com cache condicional + extração de ZIP
parsers.py # leitura dos CSVs (encoding/separador da CVM)
cvm_client.py # busca de empresas e carregamento dos demonstrativos
quarters.py # montagem da janela trimestral e derivação do 4T
models.py # estruturas de dados (Company, Quarter)
server.py # servidor MCP (FastMCP) e definição das tools
accounts.py # (inativo) mapa do plano de contas -> itens financeiros
indicators.py # (inativo) cálculo de indicadores em base anualaccounts.py and indicators.py are not imported by any tool in this version — they remain in the repository to serve as a base when indicators are reintroduced on a quarterly basis.
Análise financeira padrão dos últimos 5 anos de uma empresa.
Use quando o usuário pedir algo genérico como "analise a empresa X" sem especificar período ou indicadores. Retorna, em R$ milhões e por ano: Receita Líquida, EBIT, EBITDA estimado, Lucro Líquido, Margem Líquida, Margem EBITDA, ROE, Liquidez Corrente, Dívida Bruta, Caixa, Dívida Líquida e Dívida Líquida/EBITDA — sempre com avisos quando algum item não pôde ser calculado a partir dos demonstrativos públicos da CVM (ex: EBITDA é sempre uma estimativa, não o número "ajustado" da empresa; não há dado de cotação/valor de mercado nesta fonte). Para pedidos customizados (outro período, dado bruto, individual vs consolidado), use obter_indicadores ou obter_demonstrativo_bruto.
| Name | Required | Description | Default |
|---|---|---|---|
| nome_ou_cnpj | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description discloses key behavioral traits: EBITDA is an estimate, data comes from CVM public filings, no market cap data, and warnings are issued when items cannot be calculated. This fully informs the agent about limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded: first sentence states purpose, second guides usage, third lists returns, fourth adds caveats. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (single parameter, no output schema), the description is complete. It covers what is returned, constraints (5 years), limitations (EBITDA estimate, no market data), and provides usage guidance with alternatives.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description should compensate. The parameter 'nome_ou_cnpj' is self-explanatory, but the description does not elaborate on format or provide examples. It gives context that it's a company identifier, which is adequate but not exceptional.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a standard financial analysis of the last 5 years of a company, with a specific list of metrics. It distinguishes from siblings by specifying it is for generic requests, while alternatives handle custom periods or raw data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'use when user asks something generic like 'analyze company X' without specifying period or indicators.' It also tells when NOT to use (customized requests) and names alternative tools (obter_indicadores, obter_demonstrativo_bruto).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Busca companhias abertas no cadastro da CVM por nome, CNPJ ou código CVM.
Use esta tool antes de qualquer outra para confirmar a empresa certa: existem nomes parecidos e empresas com registro cancelado/suspenso que podem não ter demonstrativos recentes.
| Name | Required | Description | Default |
|---|---|---|---|
| limite | No | ||
| consulta | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool searches the CVM registry and that results may include companies with cancelled/suspended status, which is a useful behavioral trait. However, it does not mention any side effects, authentication needs, or return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the main action, and uses line breaks for readability. Every sentence adds value with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with 2 parameters and no output schema or annotations, the description covers purpose, usage guidance, and the main parameter. However, it omits explanation of the 'limite' parameter and return values, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains that 'consulta' can be a name, CNPJ, or CVM code, adding meaning. However, it fails to describe 'limite' (limit), which defaults to 10. Partial compensation leads to a score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (busca = searches), resource (companies in CVM registry), and search criteria (name, CNPJ, or CVM code). It distinguishes from sibling tools like analisar_empresa or obter_indicadores, which are for analysis and data retrieval, not searching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use this tool before any other to confirm the correct company, warning about similar names and cancelled/suspended registrations. This provides clear when-to-use and context-specific guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Devolve as contas contábeis brutas (código, descrição, valor em R$ milhões) de um demonstrativo publicado pela CVM, sem nenhum cálculo.
demonstrativo: um de BPA, BPP, DRE, DFC_MD, DFC_MI, DVA, DMPL, DRA. Use quando o usuário pedir algo que os indicadores prontos não cobrem, como uma conta contábil específica ou maior nível de detalhe.
| Name | Required | Description | Default |
|---|---|---|---|
| ano | Yes | ||
| consolidado | No | ||
| nome_ou_cnpj | Yes | ||
| demonstrativo | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses that the tool returns raw data 'sem nenhum cálculo' (without any calculation), indicating it is a read-only operation. However, it does not mention potential side effects or response size considerations, but the core behavior is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a list, front-loaded with the action ('Devolve as contas contábeis brutas...'). Every sentence adds value, and the structure is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, no output schema, and 3 siblings, the description covers the core functionality, usage context, and return structure (code, description, value). It lacks details on error handling or missing data behavior, but overall it is sufficiently complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 4 parameters with 0% description coverage. The description only adds meaning for the 'demonstrativo' parameter by listing valid values (BPA, BPP, DRE, etc.). Other parameters (nome_ou_cnpj, ano, consolidado) are not explained in the description, leaving their purpose inferred from context. This partially compensates for the schema gap but is insufficient for full clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns raw accounting accounts (code, description, value in BRL millions) from a CVM financial statement, without calculations. It lists specific statement types (BPA, BPP, DRE, etc.) and distinguishes itself from sibling 'obter_indicadores' by noting it provides raw detail when indicators are insufficient.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use when the user asks for something that the ready indicators don't cover, like a specific accounting account or greater level of detail.' This provides clear guidance and implies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Indicadores financeiros calculados para um intervalo de anos escolhido pelo usuário (mesmos indicadores de analisar_empresa, mas com período e versão consolidado/individual livres). Use para pedidos customizados que fogem do padrão de 5 anos.
| Name | Required | Description | Default |
|---|---|---|---|
| ano_fim | Yes | ||
| ano_inicio | Yes | ||
| consolidado | No | ||
| nome_ou_cnpj | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool returns the same indicators as analisar_empresa but with free period and consolidation choice. However, it does not mention any behavioral traits like performance implications, data freshness, or error handling, which are important for a computation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences that front-load the core purpose and usage context. No redundant or extra information is present; every word contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, no output schema, and no annotations, the description is too sparse. It does not explain the returned indicators, parameter details (especially nome_ou_cnpj and consolidado), or error conditions. A user would need additional context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameter descriptions (0% coverage). The description only partially explains ano_inicio, ano_fim, and consolidado via the phrase 'intervalo de anos' and 'versão consolidado/individual'. It does not clarify consolidado's meaning or the nome_ou_cnpj parameter at all. This is insufficient compensation for the missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool computes financial indicators for a user-chosen year range, and distinguishes it from sibling analisar_empresa by emphasizing flexibility in period and consolidation version. It uses a specific verb ('calculados') and resource ('indicadores financeiros'), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises to use this tool for custom requests that deviate from the standard 5-year period, implying that analisar_empresa should be used otherwise. This provides clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0analisar_empresabuscar_empresaobter_demonstrativo_brutoobter_indicadoresScored across 4 tools
Each tool serves a distinct purpose: buscar_empresa for company lookup, analisar_empresa for standard 5-year analysis, obter_indicadores for custom analysis, and obter_demonstrativo_bruto for raw accounting data. No overlap in functionality.
All tool names follow a consistent verb_noun pattern in Portuguese (e.g., analisar_empresa, buscar_empresa). The naming is predictable and clear.
With 4 tools, the server is well-scoped for its domain. It covers company search, standard analysis, custom analysis, and raw data retrieval without unnecessary bloat.
The tool set covers core workflows: identification, standard analysis, custom analysis, and raw data. A minor gap is the absence of a tool for comparing multiple companies or exporting data, but the essential functionality is present.
MCP server exposing the Backtest360 engine API as tools for AI agents.
Your agent needs company financials it can compute on — statements, ratios, earnings, estimates, filings and insider activity as structured data, not a PDF. **What you can ask for** • "Give me 8 quarters of income statement, balance sheet and cash flow for this ticker." • "What do analysts estimate for next quarter, and how did the last four surprise?" • "Find this exact line item across every filing." • "Who bought or sold as an insider in the last 90 days?" • "Screen for profitable companies under this valuation with growing revenue." **How to use it** Point any MCP client at https://mcp.aisa.one/marketpulse/mcp and sign in with OAuth — there is no key to create or paste. 21 tools: prices and snapshots, income statements, balance sheets, cash-flow statements, financial metrics and snapshots, earnings, analyst estimates, company facts, filings and filing items, line-item search, a screener, insider trades, macro interest rates, news, plus EDINET documents and filing digests for Japanese issuers. **Why this rather than the source** Statements as fields you can compute on, and a screener in the same place. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Read the fundamentals here, then ask the same agent what social is saying about the ticker — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/finance/mcp for equities, crypto and prediction markets in one place.
Your agent needs markets — prices and fundamentals for listed companies, the filings behind them, crypto, and what the prediction markets put the odds at. **What you can ask for** • "Pull this company's income statement, cash flow and balance sheet for the last 8 quarters." • "What did insiders buy or sell, and when?" • "Snapshot prices for these 50 tickers, then the OHLC history for the three that moved." • "What are the current odds on this event across Kalshi and Polymarket?" • "Screen for companies matching these financial criteria." **How to use it** Point any MCP client at https://mcp.aisa.one/finance/mcp and sign in with OAuth — there is no key to create or paste. 49 tools: prices and snapshots, income statements, balance sheets and cash flows, metrics and ratios, earnings and analyst estimates, filings and line-item search, insider trades, macro interest rates, news, a screener; CoinGecko spot prices, market tables, OHLC, per-venue tickers and trending; Kalshi and Polymarket markets and trades; plus EDINET filings for Japan. **Why this rather than the source** Equities, crypto and event markets behind one account, so a cross-asset question is one conversation. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Read the number here, then ask the same agent what X is saying about the ticker today — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/marketpulse/mcp · /crypto-market-data/mcp · /prediction-market-data/mcp · /stock-pulse/mcp for one slice each.
A Model Context Protocol server exposing real-time and historical Colombo Stock Exchange (CSE) data to AI agents and LLM applications. Provides quotes and OHLCV price history, full financial statements (income, balance sheet, cash flow), pre-computed technicals (moving averages, RS ratings, volume signals), macroeconomic indicators, corporate actions, and rule-based screening across CSE stocks and sector indices, everything needed to build CSE-aware trading assistants, research tools, and market-analysis agents. This is the official MCP server of www.ceyloncharts.com
