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mcp-knowledge

by io.github.getgapup

sharia_compliance_screener

Read-only

Sharia compliance screening engine for Islamic banks, Sukuk issuers, Gulf sovereign funds, halal investment managers and MENA family offices. Zero competing MCP on this vertical.

Standards supported: AAOIFI (default) | MSCI_Islamic | S&P_Sharia | DJIM

Four modes: • company — Full Sharia screen of a listed company: business activity (halal/haram/mixed) + AAOIFI financial ratios (debt/market-cap <30%, interest-assets <30%, non-compliant revenue <5%) • instrument — Sukuk / halal fund classification by ISIN or name. Maps to known Sharia boards. • sector_screen — Industry classification (halal/haram/mixed) with rationale + examples. Static AAOIFI-based map covering 40+ sectors. • financial_ratios — AAOIFI ratio computation on fetched or provided financials.

Prohibited activities screened: alcohol, gambling, pork, weapons, pornography, tobacco, conventional banking (riba), conventional insurance, adult entertainment, embryonic stem cells.

Output includes compliance_status (halal/haram/doubtful_mixed/purification_required), purification_pct when applicable, P0/P1/P2 signals, quality_score, and sources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesScreening mode. company=full listed company screen, instrument=Sukuk/fund classification, sector_screen=industry halal/haram classification, financial_ratios=AAOIFI ratio check.
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.
queryYesEntity to screen. Company name, ticker or ISIN (e.g. "Aramco", "AAPL", "tobacco", "XS1234567890").
standardNoSharia standard to apply. Default "AAOIFI" (most conservative, widely accepted by Islamic banks).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
companyNo
signalsYes
sourcesYes
instrumentNo
quality_scoreYes
sector_screenNo
standard_usedYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, indicating safe read operations. The description adds significant behavioral context: details on async mode with job polling, output fields (compliance_status, purification_pct, etc.), and the specific screening logic (e.g., AAOIFI ratios). This provides rich transparency beyond annotations.

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 well-structured and front-loaded with the core purpose. It efficiently uses bullet points and clear sections for standards, modes, prohibited activities, and output. Every sentence adds value, and the length is appropriate for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 modes, multiple standards, async behavior), the description covers all necessary aspects: target audience, supported standards, mode explanations, prohibited activities, and output fields. Since an output schema exists, the description does not need to detail return values, making it complete for an AI agent.

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?

Schema description coverage is 100%, so baseline is 3. The description adds substantial meaning beyond the schema by explaining each mode in detail, describing the default standard (AAOIFI), and outlining prohibited activities. This enhances parameter understanding, but the schema already does a solid job.

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 as a Sharia compliance screening engine, listing specific use cases (company, instrument, sector_screen, financial_ratios) and target audience (Islamic banks, Sukuk issuers, etc.). It distinguishes itself from sibling tools by claiming 'Zero competing MCP on this vertical.' This provides a specific verb+resource and clear differentiation.

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 provides detailed guidance on when to use each of the four modes and specifies supported standards. However, it does not explicitly state when NOT to use the tool or contrast it with potential alternatives. The claim of no competing tools implies it's the only choice for this vertical, but explicit exclusion criteria would improve the score.

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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