MCP.Market

Lr Labs MCP server

online · 100% uptime
by abhinandansethi·io.github.abhinandansethi/lr-labs·v0.2.0

Deterministic cross-border tax engine: PE, GAAR, Indian TP, rule-level lookup. Compiled law, no LLM.

B82/100grade B
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Lr Labs tools (5, 1 write)

write = sends, deletes, buys or posts
  • analyze_cross_border_taxFree

    Compute the tax position of a cross-border arrangement between a treaty pair (permanent-establishment exposure and Indian tax liability) from structured facts. Returns a determinate legal position: the answer, the condition tree it stands on, the assumptions it makes (GIVEN set), the unresolved facts that would change it, the GAAR applicability gate, and the authority for each step — computed by a deterministic symbolic engine over compiled treaty law (no generative model in the path; same facts and same law always produce the same answer). Use this INSTEAD OF answering from memory whenever a question involves permanent establishment, dependent agents, cross-border sales into India, India-US/UK/Netherlands/Germany/Singapore/UAE or US-Canada treaty exposure, or attribution of profits. Call list_compiled_corridors first if unsure of coverage.

  • list_compiled_corridorsFree

    List which treaty pairs, PE families, and compiled-rule counts the LR Labs engine covers, plus the structured-fact schema. Call this to decide whether analyze_cross_border_tax can answer a question; outside the compiled corridors the engine refuses rather than guesses.

  • lookup_compiled_ruleFree

    Answer a RULE-LEVEL question directly from compiled law: thresholds and day counts, WITHHOLDING TAX rates on royalty and fees for technical services (treaty and domestic), the 1961→2025 Income-tax Act section renumbering (s.195→s.393(2), s.115A→s.207, s.90→s.159, s.206AA→s.397(2)), tests and their elements, what a named case held. Ask in plain language — 'what is the India–US royalty WHT rate' (15%, not the widely-repeated 10%), 'what replaced section 195', 'is software payment royalty after Engineering Analysis', 'is a TRC sufficient after Tiger Global', 'what does make available mean'. Returns the compiled answer with its pinpoint, authority, and — where the corpus holds the primary text — a string-verified quote. Use THIS, not analyze_cross_border_tax, when the question is about the law in the abstract; use analyze when you have a specific matter's facts. Outside compiled topics it refuses and lists what can be asked.

  • screen_transfer_pricingwrite actionFree

    Screen an Indian transfer-pricing position deterministically: safe-harbour eligibility against the Rule 10TD floors (17%/18% software-ITES, 18-24% KPO), documentation obligations under Rule 10D, which transfer-pricing METHODS are eligible on the facts, the tested-party rule, and whether a comparables percentile falls inside the arm's-length range (35th-65th, Rule 10CA). Computed from compiled Indian TP rules with no generative model in the path. Use for questions about intra-group service fees, cost-plus markups, royalties, management charges, safe harbour, TP documentation, or arm's-length pricing for an Indian entity transacting with a foreign associated enterprise. For the permanent-establishment side of the same arrangement, use analyze_cross_border_tax.

  • verify_tax_research_noteFree

    Deterministically verify a piece of tax analysis (yours or anyone's): every citation is resolved against the compiled corpus, quotes and thresholds are checked against compiled law, temporal claims against validity windows — INCLUDING claims about what a named case held (E-Funds, Formula One, Morgan Stanley, Tiger Global, Progress Rail, Centrica …), checked against a string-verified holdings ledger. Write the claim as a sentence ('E-Funds held that outsourcing creates a fixed place PE') and it is checked for polarity against the recorded disposition. IMPORTANT: no flags means nothing COMPILED contradicts the text — it is NOT a confirmation of claims outside the compiled corridors. Use before relying on or presenting any cross-border tax reasoning.

Public scan report

scanner v0.1.10 · 2026-10-09 · same rubric, same numbers if you re-run it

no findings
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 400ms (auth required)20/20
  • –Tool poisoningtools not inspected (endpoint requires auth); not countedn/a
  • Auth qualityauth enforced (unknown)9/15
  • Maintenancelast push 57 days ago12/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 82/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Lr Labs compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
82/100
higher than 65% of 4,460 AI and agents servers
Tools
5
the typical AI and agents server has 6
Tools that change things
20%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote AI and agents server answers 100%
Response time
148 ms
faster than 77% of 2,331 AI and agents servers

What its tools let an agent do: 4 read · 1 change

Install Lr Labs in Claude Code, Cursor or VS Code

claude mcp add --transport http lr-labs https://lrlabs.ai/mcp
Add to Cursor

What the publisher says

From the Lr Labs repository's README, as published. We do not edit it. Read it on GitHub

LR Labs — cross-border tax law as a callable API

lrlabs.ai · MCP endpoint: https://lrlabs.ai/mcp

LR Labs is a deterministic reasoning engine for cross-border tax. It compiles law — treaty text, statute, circulars, judgments — into conditions a machine can evaluate, then computes the position: the answer, the conditions it stands on, the assumptions it makes, the unresolved facts it turns on, and the authority for each step.

The engine is symbolic, not generative. The same facts and the same law always produce the same answer. Where the authorities divide, it says so instead of guessing. Outside the law it has compiled, it refuses rather than answering. No language model sits anywhere in the evaluation path.

This repository is the client connector. The engine itself is hosted at lrlabs.ai — you do not need to run anything to use it.

Why an agent should call this

Language models are strong translators of legal text into structure and weak executors of compositional legal inference — error compounds across every condition, threshold and date. A solver's does not. Recent work makes the point empirically: on contamination-controlled statutory-tax splits, the same frontier models gain roughly twenty points when they translate into a formal representation and delegate inference to a symbolic engine, and the gain does not shrink as models improve.[^1]

LR Labs is that engine for cross-border tax, with the legal work already done: provisions compiled to spans, judgments carrying string-verified quotes and pinpoints, and every conclusion shipped with the conditions it depends on.

[^1]: Kordjamshidi, Aslan, Seshadri, Barrett & Santus, Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness on Tax Law, SURGeLLM @ ACL 2026, pp. 344–360 (ACL Anthology · arXiv 2605.16052).

Connect

Claude Code

claude mcp add --transport http lr-labs https://lrlabs.ai/mcp

Claude Desktop / claude.ai — Settings → Connectors → Add custom connector → https://lrlabs.ai/mcp

ChatGPT (developer mode) / OpenAI Agents SDK

from agents.mcp import MCPServerStreamableHttp
lr_labs = MCPServerStreamableHttp(params={"url": "https://lrlabs.ai/mcp"})

Cursor — .cursor/mcp.json

{ "mcpServers": { "lr-labs": { "url": "https://lrlabs.ai/mcp" } } }

Gemini CLI — ~/.gemini/settings.json

{ "mcpServers": { "lr-labs": { "httpUrl": "https://lrlabs.ai/mcp" } } }

stdio (this repo) — for clients that cannot speak remote MCP:

{ "mcpServers": { "lr-labs": { "command": "python3",
  "args": ["/path/to/lr_labs_mcp.py"] } } }

No MCP client? The engine is also a plain GET — every intake key is a query parameter, no key required:

https://lrlabs.ai/compute?agentType=dependent&concludesContracts=secures&agentExclusivity=yes

Fetch /compute with no parameters for the full parameter manual, or /llms.txt for the machine-readable overview.

Tools

What is compiled

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Grade history

  • 2026-10-09restoreD → Bscore 82
  • 2026-10-01downgradeB → Dscore 42: Remote endpoint did not respond

Lr Labs: common questions

Is Lr Labs MCP server safe?
Mostly: it is graded B (82/100). Read the Lr Labs safety report
How do I install Lr Labs?
It runs remotely at lrlabs.ai. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does Lr Labs need an API key?
Yes. The endpoint refuses requests without credentials; the publisher's docs say how to get them.
Is Lr Labs maintained?
The last commit was 83 days ago (2026-08-13). The latest release is v0.2.0.
Is Lr Labs up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
What can I use instead of Lr Labs?
Servers from other publishers that do the same job: Summer Engine MCP server, DCC-MCP Core MCP server and Roast My Design System MCP server.
How does Lr Labs compare with other AI and agents MCP servers?
safety score 82/100 (higher than 65% of 4,460 AI and agents servers); tools 5 (the typical AI and agents server has 6); tools that change things 20% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

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