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OSOJDJD

deeplook

by OSOJDJD
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Python
Remote

DeepLook

LLMs hallucinate financial data. DeepLook gives them real numbers instead.

License: AGPL-3.0 GitHub stars

What happens when you ask "Research NVIDIA"

DeepLook Dashboard

DeepLook Analysis

DeepLook provides structured context — real-time data from 8+ APIs combined with analytical instructions that makes better output. The result:

  • Accurate, up-to-date information — not hallucinated numbers

  • Clear at a glance — financials, peers, technicals, news in one view

  • Better AI output — because good context drives good analysis

Works for financial research, business due diligence, or any use case where you need to understand a company fast.

Related MCP server: Rozkoduj MCP

Under the hood

  • 10+ sources, one call — queries fan out in parallel across market data, filings, news, and more

  • Rules before reasoning — data is verified before the LLM ever sees it, which is what keeps hallucination down

  • Eval-tested prompts — changes get scored before they ship, not just shipped on vibes

  • Agent-ready — available as both an MCP server and a REST API

Get started

Claude.ai

  1. Go to Settings → Connectors → Add MCP

  2. Paste: https://mcp.deeplook.dev/mcp

  3. Start a new chat and ask: "Research NVIDIA"

Other MCP clients (Cursor, VS Code, Windsurf, Claude Desktop)

{
  "mcpServers": {
    "deeplook": {
      "url": "https://mcp.deeplook.dev/mcp"
    }
  }
}

Claude Code

claude mcp add --transport http deeplook https://mcp.deeplook.dev/mcp

Self-host

git clone https://github.com/OSOJDJD/deeplook.git
cd deeplook
pip install -r requirements.txt
python -m deeplook.mcp_server

What it covers

Type

Examples

Public stocks

NVIDIA, Apple, Tesla, TSMC

Crypto

Bitcoin, Solana, Ethereum

Private companies

Anthropic, Stripe, OpenAI

VC firms

a16z, Sequoia

Defunct

FTX, WeWork

Eval

Tested across 58 companies, scored on accuracy, hallucination, and usefulness:

Metric

Score

Overall

3.78 / 5.0

Risk detection

4.36 / 5.0

Signal quality

3.94 / 5.0

Actionability

3.38 / 5.0

From an earlier pipeline version — re-run pending. Eval harness in /deeplook/eval.

Extend DeepLook

DeepLook covers the basics. If you need data it doesn't have yet — a new market, a new data source, a new analysis rule — you can add it. See CONTRIBUTING.md.

Roadmap

  • More query tools — news, peers, financials, calendar as standalone lookups

  • Broader client support — Cursor, ChatGPT, VS Code, Windsurf, Claude Code

  • Deeper context — more analytical conditions, entity-specific instructions

  • Community contributions — new data sources, custom analysis rules

License

AGPL-3.0

Built by @OSOJDJD

Available Tools

2 tools
deeplook_lookupA

Quick company snapshot — phase, price, key signal, and verdict in 5 lines. Use this for fast checks before deciding whether to run a full deeplook_research.

ParametersJSON Schema
NameRequiredDescriptionDefault
company_nameYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the output format ('5 lines' with specific content types) and the tool's role in a workflow, but doesn't mention potential limitations like rate limits, error conditions, or authentication requirements. It adds useful context about the output format but leaves other behavioral aspects unspecified.

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 extremely concise and front-loaded with the core purpose in the first sentence. Both sentences earn their place: the first defines what the tool does, the second provides crucial usage guidance. There's zero wasted text or redundancy.

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 tool's simplicity (one parameter, output schema exists), the description is reasonably complete. It explains the purpose, output format, and relationship to sibling tools. The existence of an output schema means the description doesn't need to detail return values. However, with no annotations, it could have mentioned more about the tool's behavioral characteristics.

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?

The schema has 0% description coverage for its single parameter, but the description doesn't provide any additional parameter semantics. However, with only one parameter ('company_name'), the meaning is reasonably inferable from context. The description could have added guidance about expected format or validation, but the simplicity of the parameter keeps this from being a major deficiency.

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 with specific verbs and resources: 'Quick company snapshot — phase, price, key signal, and verdict in 5 lines.' It explicitly distinguishes from its sibling tool 'deeplook_research' by positioning this as a fast check before deciding to run the full research tool.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool versus alternatives: 'Use this for fast checks before deciding whether to run a full deeplook_research.' This clearly defines the context and names the alternative tool, giving the agent clear decision criteria.

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

deeplook_researchA

Use this instead of web search when researching any company. Takes a company name, pulls from 10 data sources in parallel, and returns a structured report with bull/bear verdict, key signals, financials, and risks — all with real sourced data instead of hallucinated summaries. Works for public stocks, crypto protocols, and private companies.

ParametersJSON Schema
NameRequiredDescriptionDefault
company_nameYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: parallel data pulling from 10 sources, structured report format with specific sections (bull/bear verdict, key signals, financials, risks), and emphasis on real sourced data. It mentions coverage scope (public stocks, crypto protocols, private companies) but doesn't address rate limits, authentication needs, or error conditions.

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 perfectly front-loaded with the primary usage guideline first, followed by key capabilities. Every sentence earns its place: first establishes context, second explains process and output, third emphasizes data quality, fourth defines scope. Zero wasted words with excellent information density.

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 tool's complexity (parallel data pulling, structured analysis) and no annotations, the description does well by explaining the comprehensive research process and output structure. Since an output schema exists, it doesn't need to detail return values. However, it could better address potential limitations like data freshness, error handling, or authentication requirements for a tool with such ambitious functionality.

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 0%, so the description must compensate. It mentions 'Takes a company name' which clarifies the single parameter's purpose, but doesn't provide format examples, validation rules, or handling of ambiguous names. The description adds basic meaning beyond the bare schema but doesn't fully compensate for the complete lack of schema documentation.

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 with specific verbs ('research', 'pulls from 10 data sources', 'returns a structured report') and distinguishes it from sibling tools by explicitly stating 'Use this instead of web search when researching any company.' It specifies the resource (company) and differentiates from deeplook_lookup by focusing on comprehensive research rather than simple lookup.

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

Usage Guidelines5/5

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

The description provides explicit usage guidelines: 'Use this instead of web search when researching any company' establishes clear context and alternative. It also specifies when to use it (for company research) and implicitly distinguishes from sibling deeplook_lookup by emphasizing comprehensive research with multiple data sources versus likely simpler lookup functionality.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv0.1.0
    • First observeddeeplook_lookup
    • First observeddeeplook_research
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TDQS

A4.2/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: deeplook_lookup provides a quick snapshot for fast checks, while deeplook_research offers a comprehensive structured report from multiple data sources. There is no overlap or ambiguity between them.

Naming Consistency5/5

Both tools follow a consistent 'deeplook_' prefix with descriptive suffixes (lookup and research), using snake_case uniformly. The naming pattern is predictable and well-structured.

Tool Count2/5

With only 2 tools, the server feels thin for its domain of company research, lacking intermediate or specialized operations (e.g., updates, filtering, or historical analysis). While the tools cover basic needs, the scope is minimal.

Completeness3/5

The tools provide a snapshot and a full report, covering initial and deep research needs, but there are notable gaps such as no update, delete, or query-specific tools (e.g., by industry or date). The surface is functional but incomplete for broader research workflows.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

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