Click on "Install 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., "@@oilflow/mcp-server Should I be worried about a 50,000 MT EN590 cargo from Iraq to Kenya with an unfamiliar seller?"
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.
Model Context Protocol (MCP) server for the OilFlow Network compliance APIs.
It turns OilFlow into a set of tools that any MCP-compatible AI agent (Claude Desktop, Claude Code, Cursor, or your own agent built on the MCP SDK) can call directly.
Three tools need no API key at all (since 0.3.0; the server used to refuse to start without one). 0.3.1 adds the MCP registry name io.github.rafaemush/oilflow-mcp-server and points the package at its public repository; no behaviour change.
Source of truth: this package is developed inside the OilFlow monorepo and mirrored to github.com/rafaemush/oilflow-mcp-server on every release. Issues go there.
⚡ predeal_preview: the full pre-deal clearance verdict on a proposed deal, free, 5 per day. Returns a receipt_id + verify_url.
🧾 verify_receipt: check an OilFlow screening receipt someone handed you, and see exactly what it did and did not cover.
🔑 request_sandbox_key: mint a free 30-day key (with the end user's consent) to unlock the rest.
With a key:
🛡️ kyc_screen — counterparty KYC: cluster-blocklist + regulatory tradability + the 7-step pipeline (8-list sanctions screening; PEP is NOT screened)
🌍 regulatory_check / regulatory_countries / regulatory_products — "can I trade X from country A?" across the 235-jurisdiction regulatory matrix
🚩 cluster_check / clusters_list — look up a counterparty against the first-party-investigated Scam Cluster Intelligence Feed
📄 lc_validate — Letter-of-Credit discrepancy detection against UCP 600
🕸️ ubo_screen / ubo_graph — beneficial-ownership graph traversal + shell-company pattern flags
⚡ predeal_check — sub-30s pre-deal clearance probability + restructure suggestion for a proposed physical-commodity deal
Every tool is a thin passthrough to the OilFlow API. Results are decision-support for a compliance professional, not legal advice; cluster suspected rows are publicly reported leads, not OilFlow-confirmed fraud.
npm install -g @oilflow/mcp-server
# or run on demand with npx (no install):
npx @oilflow/mcp-serverRequires Node ≥ 18.
Related MCP server: agentmail
The server reads two environment variables, and neither is required:
Variable | Required | Default | Notes |
| no | none | An |
| — |
| Override only for self-hosted/staging. |
Sandbox keys cover the regulatory and cluster tools.
kyc_screen,ubo_screen,ubo_graph,lc_validateandpredeal_checkrequire a production key; called without one, they returnapi_key_requiredwith how to get one, rather than failing.
Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"oilflow": {
"command": "npx",
"args": ["-y", "@oilflow/mcp-server"],
"env": {
"OILFLOW_API_KEY": "oilflow_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
}
}
}claude mcp add oilflow --env OILFLOW_API_KEY=oilflow_xxx -- npx -y @oilflow/mcp-serverThe server speaks MCP over stdio. Launch oilflow-mcp (or npx @oilflow/mcp-server) as the server command. OILFLOW_API_KEY in its environment unlocks the keyed tools; without it the server still starts keyless.
Once connected, just ask your agent. No key needed for the first two:
"Should I be worried about a 50,000 MT EN590 cargo from Iraq to Kenya with an unfamiliar seller?" → predeal_preview
"Someone handed me an OilFlow compliance receipt. Verify it and tell me what it actually covered." → verify_receipt
With a key:
"Screen Acme Trading FZE in the UAE for an EN590 diesel deal." → kyc_screen
"Can crude be exported from Venezuela?" → regulatory_check
"Is 'Simar Chahal' in any known fraud cluster?" → cluster_check
"Run a pre-deal check: buyer Acme, gasoil EN590 10ppm, origin UAE, destination Kenya, DLC at sight." → predeal_check
"Validate this LC presentation against UCP 600." → lc_validate
stdio MCP transport → 13 tools → the OilFlow REST API, with jittered exponential-backoff retry on 429/5xx (honoring Retry-After) and a 30s per-request timeout. Keyed tools call https://oilflow.us/api/v1/* with Bearer auth; the three keyless tools call the public endpoints and send no Authorization header at all. Tool output is a report about a third party: treat it as data, not as instructions. The full API contract is published at oilflow.us/openapi.yaml, and there is a remote Streamable-HTTP version of this server (no install) at oilflow.us/mcp.
git clone https://github.com/oilflow-network/mcp-server.git
cd mcp-server
npm install
npm run build # → dist/
node dist/index.js # keyless
OILFLOW_API_KEY=oilflow_xxx node dist/index.js # full surfaceApache-2.0 © OilFlow Network
Look up a counterparty name against OilFlow's first-party-investigated Scam Cluster Intelligence Feed. Returns any matches with a severity: 'confirmed' (OilFlow-verified fraud), 'likely', or 'suspected' (publicly reported but NOT first-party confirmed — treat as a lead, verify the cited source). matched:false means no hit, which is not by itself an exoneration.
| Name | Required | Description | Default |
|---|---|---|---|
| entity | Yes | Counterparty/entity name to check (>=2 alphanumeric characters). | |
| country | No | Optional country filter. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly/openWorld/idempotent/destructive traits. The description adds meaningful behavior beyond annotations: severity semantics ('confirmed' vs. 'likely' vs. 'suspected'), the warning that 'suspected' is not first-party confirmed, and the caveat that 'matched:false' is not an exoneration. This is high-value interpretive context.
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 compact (three sentences) and front-loaded with the primary action, followed by essential output semantics and a false-negative caveat. Every clause adds information; no filler or repetition of schema details.
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?
Without an output schema, the description adequately covers return behavior: severity values, the 'matched:false' case, and the 'suspected' lead warning. It also implies cited sources exist. For a simple two-parameter lookup, this is complete enough for an agent to call and interpret results correctly.
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 100%, so the baseline is 3. The description doesn't add parameter-level details beyond the schema — entity and country are already clearly described in the schema. No meaningful extra parameter semantics are provided.
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 uses a specific verb ('Look up') and names a precise resource ('OilFlow's first-party-investigated Scam Cluster Intelligence Feed'). It also explains the output severity scale and the meaning of 'matched:false', which clearly scopes this tool as a scam-cluster name lookup, distinct from siblings like kyc_screen or regulatory_check.
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 gives a clear context: checking counterparty names against a specialized fraud feed. However it does not explicitly name sibling tools or specify when not to use it. Still, the context strongly implies its niche vs. other screen types, so it earns a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List entries from the Scam Cluster Intelligence Feed, optionally filtered by severity, country, or date. Useful for browsing known fraud clusters. Severity 'suspected' rows are publicly reported leads, not OilFlow-confirmed.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | ISO date (YYYY-MM-DD) — only entries added on/after this date. Optional. | |
| country | No | Filter by country. Optional. | |
| severity | No | Filter by severity. Optional. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already set readOnlyHint=true, covering the safety profile. The description adds a valuable data-quality caveat: 'Severity 'suspected' rows are publicly reported leads, not OilFlow-confirmed,' which helps the agent interpret results correctly.
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?
Three sentences, each earning its place: main action, use case, and a critical caveat. The primary operation is front-loaded, with no redundant wording.
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 read-only list tool with no output schema, the description covers the feed source, filter options, and severity semantics. It does not mention pagination or return-field details, but those are not essential for a correct first invocation.
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 100%, with descriptions for all three parameters (since, country, severity). The description only restates the availability of these filters without adding format or constraint details beyond the schema.
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 a specific verb and resource: 'List entries from the Scam Cluster Intelligence Feed,' and identifies optional filters. While it does not explicitly contrast with sibling tools like cluster_check, the operation is 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 phrase 'Useful for browsing known fraud clusters' provides a clear context for when to call this tool. It does not name alternatives or exclusions, but the use case is sufficiently clear for a read-only feed-browsing operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run an OilFlow counterparty KYC screen: cluster-blocklist match, regulatory tradability, and the 7-step registration pipeline with 8-list sanctions screening (OFAC SDN, OFAC Consolidated, UN, EU, UK HMT, Canada SEMA, AU DFAT, Swiss SECO). PEP screening is NOT shipped — coverage is sanctions-only, so a politically exposed person who is not also designated will not be flagged; tell the user this. Sanctions/registration/asset/footprint steps run out-of-band and return as 'queued' with a poll_url. Returns a verdict (pass/review/fail) with per-check evidence. Decision-support for a compliance analyst, not a legal determination. Requires a production API key.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | Full country name (e.g. 'United Arab Emirates'). Optional. | |
| product | No | Commodity/product the deal involves (e.g. 'EN590 diesel'). Optional. | |
| directors | No | Named directors/officers to include in screening. Optional. | |
| company_name | Yes | Legal/trading name of the counterparty to screen. | |
| listing_type | No | Counterparty's side of the trade. Defaults to 'demand'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses critical behavioral traits: PEP screening is not shipped and users must be told, sanctions/registration/asset/footprint steps run out-of-band and return as 'queued' with a poll_url, and a verdict plus per-check evidence is returned. It also clarifies the tool is decision-support, not a legal determination. No contradiction with the annotations exists.
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 dense but every sentence carries necessary information: scope, critical limitation, async behavior, return shape, intended use, and prerequisite. The most important action and scope are front-loaded, and the caveats are placed where they are most actionable.
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 complex tool with no output schema, the description is remarkably complete: it names the data sources, explains the async queuing behavior and poll_url, defines the return verdict values, states the PEP limitation, sets expectations about legal authority, and notes the API key requirement. An agent has enough context to invoke it correctly and communicate results honestly.
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 100%, so the schema already documents all five parameters fully. The description does not add much parameter-level meaning, but it provides useful context about what the screen does with the supplied company information. This meets the baseline for full schema coverage.
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 states an explicit action ('Run an OilFlow counterparty KYC screen') and enumerates the concrete scope: cluster-blocklist match, regulatory tradability, a 7-step registration pipeline, and 8-list sanctions screening. The resource is clearly distinguished from sibling tools like regulatory_check, cluster_check, and ubo_screen by framing it as a comprehensive KYC pipeline rather than a single-purpose check.
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 gives clear context such as requiring a production API key, being decision-support for a compliance analyst, and not being a legal determination, and it warns that PEP screening is not included. However, it does not explicitly state when to prefer this tool over sibling alternatives like ubo_screen or regulatory_check, nor does it name any alternatives or exclusion conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate a Letter of Credit presentation (LC + commercial invoice + bill of lading) against UCP 600 rules. Returns discrepancies with severity, an overall recommendation (honor/inquiry/refuse), and the UCP 600 articles cited. Requires a production API key (sandbox keys get 403). Output is DRAFT decision-support for a documentary-credit examiner — not a legal opinion.
| Name | Required | Description | Default |
|---|---|---|---|
| bl | Yes | Bill-of-lading fields (currency, port_of_loading, issue_date, shipped_on_board, ...). | |
| lc | Yes | Letter-of-credit fields (currency, beneficiary, amount, expiry_date, port_of_loading, ...). | |
| invoice | Yes | Commercial-invoice fields (total_amount, currency, seller_name, presentation_date, ...). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate readOnly, idempotent, and non-destructive behavior, and the description adds meaningful behavioral context beyond them: it discloses the sandbox 403 failure mode, defines the output shape (discrepancies with severity, honor/inquiry/refuse recommendation, cited UCP 600 articles), and explicitly limits the output to draft decision-support rather than a legal opinion. This is substantial supplementary transparency.
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?
Three sentences with no filler: the first states the core action and inputs, the second summarizes outputs, and the third conveys authentication constraints and an important limitation. Everything earns its place and the most critical information is front-loaded.
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 there is no output schema, the description appropriately explains what the tool returns: discrepancies with severity, an overall recommendation, and the UCP 600 articles cited. It also covers the authentication requirement and the non-legal nature of the output. An agent has enough context to invoke the tool correctly and interpret its result.
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 100%, so the parameter objects are already documented in the schema. The description adds only that the three objects together form the presentation being validated, which is helpful framing but does not provide additional per-parameter meaning beyond what the schema already contains. Baseline 3 is appropriate.
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 uses a specific verb ('Validate') and clearly names the resource and scope: a Letter of Credit presentation consisting of LC, invoice, and bill of lading, checked against UCP 600 rules. It is immediately distinguishable from the unrelated sibling tools in this toolset.
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 clearly establishes the context in which this tool is used — validating a documentary-credit presentation under UCP 600 — and provides an important prerequisite: a production API key is required and sandbox keys will fail with 403. It does not explicitly name alternatives or exclusions, but no sibling tool competes for this use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a Pre-Deal Compliance Copilot check for a front-office originator: a sub-30s clearance probability (0-100) and verdict tier (clear/review/heavy_friction/block) for a proposed physical-commodity deal, with blockers, a restructure suggestion, and an estimated post-restructure clearance. Runs cluster, regulatory, adverse-media, sanctions, and verified-profile primitives. The response is self-describing about its own reliability: verdict_source is "claude" for a full synthesis or "rule_based" when the deterministic fallback ran, and evidence_gaps lists evidence sources that could not be consulted for this verdict — surface both to the user rather than presenting the score bare. Decision-support that estimates how compliance is likely to treat the deal — not a clearance itself and not legal advice; carries a disclaimer in the response. Requires a production API key; use predeal_preview for a free verdict without one.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | Free-form deal context. Optional. | |
| product | Yes | Product/spec, e.g. 'gasoil EN590 10ppm'. | |
| volume_mt | No | Deal volume in metric tonnes (> 0). Optional. | |
| origin_country | Yes | Country of origin. | |
| counterparty_name | Yes | Name of the counterparty in the proposed deal. | |
| counterparty_role | Yes | The counterparty's role in the deal. | |
| payment_structure | No | Payment terms, e.g. 'DLC at sight + SBLC'. Optional. | |
| destination_country | No | Destination country. Optional. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal, so the description carries the behavioral burden, and it does so thoroughly. It discloses the deterministic fallback behavior (verdict_source = 'rule_based'), the evidence-gap mechanism, the production API key requirement, the decision-support nature rather than actual clearance, the disclaimer, and the primitives run. All this is beyond what annotations alone provide.
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 dense but every sentence earns its place: outputs, primitives, reliability self-description, caveats, API-key requirement, and the preview alternative. It is front-loaded with the primary purpose and output contract in the first sentence.
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?
With no output schema, the description must explain return values, and it does: probability, verdict tier, blockers, restructure suggestion, post-restructure clearance, verdict_source, and evidence_gaps. It also covers authentication, disclaimers, fallback behavior, and the sibling alternative, making the tool fully callable by an agent without additional inference.
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 100%, so the schema already fully documents all 8 parameters. The description adds useful domain context (physical-commodity deal, front-office originator) but no parameter-specific semantics beyond the schema, matching the baseline 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 states a specific verb and resource ('Run a Pre-Deal Compliance Copilot check') and enumerates the exact outputs: clearance probability, verdict tier, blockers, restructure suggestion, and post-restructure clearance. It also distinguishes itself from siblings by naming the sub-tools it orchestrates (cluster, regulatory, adverse-media, sanctions, verified-profile) and by contrasting with predeal_preview.
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 provides explicit when-to-use guidance ('for a front-office originator'), an explicit alternative ('use predeal_preview for a free verdict without one'), and a clear exclusion ('not a clearance itself and not legal advice'). It also gives user-facing handling instructions for verdict_source and evidence_gaps, which goes beyond typical usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run OilFlow's Pre-Deal Compliance check on a proposed physical-commodity deal and get a clearance probability (0-100) and verdict tier (clear/review/heavy_friction/block) with named blockers and a restructure suggestion. NO API KEY REQUIRED: this is the full verdict, not a teaser. QUOTA: 5 verdicts per caller per 24 hours; on exhaustion the error is demo_limit_reached, which means the free allowance is used up and NOT that OilFlow is down. Returns receipt_id and verify_url, so the user can hand anyone a signed record of the check, and verdict_source ("claude" = full synthesis, "rule_based" = deterministic fallback) plus evidence_gaps: relay both, because a score without them overstates its own confidence. REQUIRED INPUTS: counterparty_name, counterparty_role (buyer or seller), product and origin_country; the verdict endpoint rejects a call missing any of them, so ask the user for the missing one rather than guessing a side. Decision-support estimating how a compliance desk is likely to treat the deal. Not a clearance and not legal advice; the response carries a disclaimer, pass it on. For the keyed version with no daily cap and the full input set, use predeal_check.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | e.g. 'EN590', 'Crude Oil', 'Jet A-1'. | |
| volume_mt | No | Deal volume in metric tonnes (> 0). Optional. | |
| origin_country | Yes | Country of origin. | |
| counterparty_name | Yes | The counterparty being assessed. | |
| counterparty_role | Yes | Which side of the deal the counterparty is on. REQUIRED: the verdict differs by side, so there is no safe default to guess. Ask the user if the deal description does not say. | |
| destination_country | No | Destination country. Optional. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes far beyond the annotations by disclosing the 5-per-24-hour quota, the exact error code on exhaustion, the two verdict_source modes and why the agent should relay evidence_gaps, and the fact that the response carries a legal disclaimer that must be passed on. It also clarifies that this is decision-support, not a clearance or legal advice. Annotations are consistent with this mutating-but-not-destructive profile, so no contradiction exists.
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 long but every sentence carries operational value: main result first, then quota semantics, then output fields the agent must relay, then required-input handling, then the alternative. There is no filler or repetition of schema content. Given that no output schema exists, this density is justified and well-organized.
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 tool with no output schema and six parameters, the description covers all critical context an agent needs: what the verdict contains, how quota errors manifest, what fields to relay to the user, which inputs are mandatory, and which sibling tool to use for the higher-tier version. Nothing essential for correct invocation is missing.
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 already 100%, so the baseline is 3. The description adds meaningful semantic context beyond the schema: it enumerates the required inputs, explains that counterparty_role has no safe default because the verdict differs by side, and warns that the endpoint rejects calls missing required fields. This is a genuine supplement to the schema rather than mere repetition.
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 names the specific operation (OilFlow Pre-Deal Compliance check) and the exact outputs (probability 0-100, verdict tier, blockers, restructure suggestion, receipt_id, verify_url, verdict_source, evidence_gaps). It also differentiates itself from the sibling predeal_check by explicitly labeling this the free, quota-limited version. An agent can immediately tell what this tool does and how it differs from nearby tools.
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 gives explicit when-to-use guidance: use this for the free quota-limited verdict, and names the alternative predeal_check for the keyed version with no daily cap and full input set. It also instructs the agent to ask the user for missing required inputs rather than guessing, and clarifies that the demo_limit_reached error means quota exhaustion, not an outage. This leaves no ambiguity about when to invoke this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check whether a given commodity can be supplied/bought from a given jurisdiction under OilFlow's 235-jurisdiction regulatory matrix. Returns allowed:true/false plus any blockers. Compiled regulatory guidance, not legal advice — confirm with the relevant national regulator before acting.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | Country name or ISO-2 code (e.g. 'Nigeria' or 'NG'). | |
| product | Yes | Product keyword: crude, refined, diesel, gasoline, gasoil, fuel oil, fuel, naphtha, jet, kerosene, lpg, lng, or bitumen. | |
| listing_type | No | Trade side. Defaults to 'demand'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool read-only and idempotent. The description adds meaningful extra context: results are compiled regulatory guidance, not legal advice, and the user should confirm with a national regulator. It also discloses the non-legal nature of the matrix.
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 that front-load the core action and return behavior, then add a necessary caveat. No wasted words; the legal disclaimer earns its place given the domain.
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 low-complexity read-only lookup with fully documented parameters and no output schema, the description is complete: it names the inputs, the output, and the operational limitation. The agent has enough to call it correctly without further elaboration.
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 100%, so the baseline is 3. The description's supplied/bought wording loosely maps to the listing_type supply/demand distinction, but it does not add meaningful detail beyond what the schema already documents for country, product, and listing_type.
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's exact purpose: checking whether a commodity can be supplied/bought in a jurisdiction using OilFlow's regulatory matrix. It also names the return shape (allowed true/false plus blockers), which separates it from listing-oriented siblings like regulatory_countries.
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 gives a clear usage context: use this when you need a tradability determination for a specific country/product combination. It does not explicitly mention when to prefer regulatory_countries or regulatory_products, but the conditions for use are unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List every jurisdiction covered by the OilFlow regulatory matrix (235 jurisdictions). Returns a count and the country list with slugs for use in regulatory_check.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destrructiveHint=false, so the safety profile is covered. The description adds value by specifying that the result includes a count and the country list, plus the exact jurisdiction count of 235. No hidden side effects or auth concerns are relevant for this read-only listing operation.
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 concise sentences with no filler. It front-loads the core purpose, includes the key count, and ends with the actionable downstream use, making it easy to process quickly.
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 zero-parameter, no-output-schema listing tool, the description covers the essential facts: what is listed, how many items, what the items include, and how to use the result. There is no meaningful missing context.
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 tool has zero parameters and an empty input schema with 100% schema description coverage, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because no parameter guidance is needed.
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 uses a specific verb ('List') and a precise resource ('jurisdictions covered by the OilFlow regulatory matrix'), and it states the exact output (a count and country list with slugs). This clearly separates it from sibling tools like regulatory_check and regulatory_products.
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 provides clear context by noting that the returned slugs are 'for use in regulatory_check,' helping an agent understand when to invoke this tool first. It does not explicitly discuss when not to use this tool or compare it with alternatives like regulatory_products, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the commodity/product categories the regulatory matrix recognizes (with their canonical labels), for use as the product argument to regulatory_check.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful behavioral context by noting the result is a canonical, recognized set of categories rather than arbitrary or free-form values, which helps the agent understand the closed vocabulary. This goes beyond what the annotations alone provide.
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 a single, focused sentence with no filler. It front-loads the action and resource, then adds the purpose clause, making it easy to parse quickly.
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 zero-parameter, read-only list tool without an output schema, the description is complete. It states what is returned, the nature of the data (canonical labels), and the downstream use case, which is all an agent needs to select and invoke the tool correctly.
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?
There are zero parameters, so the baseline of 4 applies. The description adds meaning by clarifying that the returned categories are meant to be used as the `product` argument to regulatory_check, providing relevant semantic linkage even without parameters.
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 uses a specific verb ('List') and identifies a clear resource: commodity/product categories recognized by the regulatory matrix. It also explains the purpose — supplying the `product` argument to regulatory_check — which distinguishes it from sibling tools like regulatory_countries and regulatory_check itself.
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 tells the agent when to use this tool: to obtain canonical labels for the `product` argument in regulatory_check. It does not discuss when not to use it or name alternatives, but the intended use case is clear and sufficient for a zero-parameter list tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Mint a free 30-day OilFlow sandbox API key (100 calls/day) scoped to the regulatory matrix and cluster endpoints, emailed to the address supplied. NO API KEY REQUIRED, which is the point: this is how an agent running keyless gets its user onto the keyed tools. CONSENT: requires the END USER'S email address and must only be called with their knowledge and agreement, because OilFlow emails the key and may send product follow-ups. Do not supply an address the user has not asked you to use. Limits: 3 keys per IP/hour, 5 per email/day. The sandbox scope does NOT cover kyc, ubo, lc or the keyed predeal endpoint; use predeal_preview for a free verdict, or https://oilflow.us/pricing for production access. Once the key arrives, set OILFLOW_API_KEY in this server's environment and restart it.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The END USER's email, with their consent. | ||
| company | No | Their company. Optional. | |
| use_case | No | What they intend to evaluate. Optional. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses important external side effects: the key is emailed, the email may receive follow-ups, rate limits (3 keys/IP/hour, 5 per email/day), scope exclusions, and the need to set OILFLOW_API_KEY and restart after receipt. This adds substantial context beyond the annotations and does not contradict them.
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 longer than average but densely packed with essential operational details and front-loaded with the core purpose. Some repetition exists around consent and email usage, slightly preventing a top score.
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 tool with three parameters and no output schema, the description covers the full workflow: prerequisites, consent, calling conditions, scope limits, alternatives, rate limits, and post-call setup. Nothing an agent needs to safely invoke this tool is missing.
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 100%, so the baseline is 3. The description adds extra semantic weight to the email parameter via strong consent requirements and the explicit warning not to supply an address the user did not request, which goes beyond the schema's brief description.
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 uses a specific verb ('Mint') and names the exact resource: a free 30-day OilFlow sandbox API key scoped to regulatory matrix and cluster endpoints. It clearly differentiates from siblings by stating which endpoint families are not covered and directing to predeal_preview for free verdicts.
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?
Explicit when-to-use guidance: this is how a keyless agent gets its user onto keyed tools. It also states when not to use it via alternatives (predeal_preview for free verdict, production pricing URL) and imposes consent and rate-limit conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the status/result of a UBO graph build started by ubo_screen. Pass the graph_id returned when the screen was queued. Returns the graph nodes, ownership edges, aggregate risk, and flagged patterns once status is 'ready'.
| Name | Required | Description | Default |
|---|---|---|---|
| graph_id | Yes | The graph_id returned by ubo_screen. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable detail beyond annotations by specifying the async status expectation ('once status is ready') and the returned graph content (nodes, edges, risk, flagged patterns).
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?
Three tight sentences front-load the action and resource, then give the prerequisite, then describe the outcome. No filler or repetition of annotation fields.
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 single-parameter read tool, the description is complete: it states the prerequisite, the readiness condition, and the returned content despite lacking an output schema. An agent has enough to call and interpret the tool correctly.
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 100%, and the schema already explains graph_id as returned by ubo_screen. The description reinforces that same relationship but does not add new format, constraints, or default information, so a baseline 3 is appropriate.
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 uses a specific verb ('Fetch') and names the exact resource: the status/result of a UBO graph build initiated by ubo_screen. It clearly distinguishes itself from the related sibling ubo_screen by focusing on retrieval rather than creation.
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?
It gives clear context: call this only after ubo_screen has returned a graph_id, and wait until status is 'ready' for the full result. It does not explicitly state when not to use it or name alternative tools, so it stops one step short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Start a beneficial-ownership (UBO) graph traversal for an entity: walks ownership across registries, flags shell-company patterns, and screens nodes for sanctions exposure (8 lists; PEP is NOT screened). Applies the OFAC 50%-rule to derived ownership. Returns the cached graph immediately when available, otherwise a graph_id to poll with ubo_graph. Coverage caveat to pass on: there is no functioning US domestic UBO registry post-March-2025, so beneficial-ownership coverage is partial and every result says so. Flagged patterns are structural signals to review, not a verdict on any individual. Requires a production API key.
| Name | Required | Description | Default |
|---|---|---|---|
| force_refresh | No | Bypass the cache and rebuild the graph. Optional, default false. | |
| root_entity_name | Yes | The entity to start the ownership traversal from. | |
| root_jurisdiction | Yes | Jurisdiction (ISO-2 or slug) to disambiguate the root entity. REQUIRED: the endpoint rejects a call without it, because two registries can hold the same company name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond the annotations, disclosing the 8 sanctions lists, the PEP exclusion, the OFAC 50% rule, cache-return behavior, the partial US registry coverage post-March-2025, and the interpretive warning that flagged patterns are signals rather than verdicts. This is rich behavioral context that an agent needs before invoking and before passing results to a user.
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 longer than a typical tool description, but every sentence carries useful information: caching behavior, polling path, regulatory limitations, and authentication requirements. The main action is front-loaded, with caveats placed later. It is dense but not bloated, and the length is justified by the tool's complexity.
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?
Even without an output schema, the description explains what a response contains: either a cached graph or a graph_id to poll with ubo_graph. It also provides the critical coverage caveat to relay, the production key requirement, and the risk-interpretation guardrail, making the description effectively complete for correct invocation and result handling.
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 100% parameter description coverage, so the schema already documents root_entity_name, root_jurisdiction, and force_refresh. The tool description does not need to repeat parameter details and does not add much parameter-specific meaning, so the baseline of 3 is appropriate.
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 opens with the specific action 'Start a beneficial-ownership (UBO) graph traversal for an entity' and explains what that traversal does: walks registries, flags shell-company patterns, and screens for sanctions exposure. It also distinguishes itself from the sibling ubo_graph by positioning this tool as the initiator and ubo_graph as the polling target, and warns that PEP is not screened.
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 gives clear usage context: use this tool to start a UBO graph, and when the cache is unavailable, poll ubo_graph with the returned graph_id. It also explains the production API key requirement and the coverage caveat to pass on. It does not explicitly contrast with kyc_screen or other sibling tools beyond the PEP exclusion, stopping short of a full when-to-use/when-not-to-use guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Independently verify an OilFlow screening receipt. NO API KEY REQUIRED. Given a receipt id (a UUID, from receipt_id/verify_url on a verdict, or from a receipt the user was handed), returns the receipt, its canonical payload string and an HMAC-SHA256 signature, so the user's own auditor can check it without trusting OilFlow's server at audit time. receipt_type says which kind of record it is: a sanctions_screen carries the list set it checked, a predeal_verdict carries the pre-deal tier plus the depth at which it consulted sanctions data. Read coverage back to the user: sanctions-only means PEP screening is not shipped, so a politically exposed person who is not also designated will not be flagged. Use this whenever a user has been HANDED a compliance receipt and wants to know whether it is genuine and what it actually covered.
| Name | Required | Description | Default |
|---|---|---|---|
| receipt_id | Yes | The receipt / screening id (UUID). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal read-only and non-destructive behavior, and the description adds substantial context beyond that: no API key required, the returned signature enables independent audit, receipt_type semantics for sanctions_screen vs predeal_verdict, and the practical limitation that sanctions-only coverage does not flag PEPs who are not also designated. This is genuinely transparent.
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 long but every sentence contributes: purpose, auth requirement, return contents, semantic meaning of receipt_type, the PEP limitation, and the triggering user context. It is front-loaded with the core purpose and keeps the most operationally important detail ('NO API KEY REQUIRED') near the start.
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 single-parameter tool with no output schema, the description covers the input origin, return shape, key field semantics, trust implications, and the user-facing caveat about coverage. Nothing essential is missing for an agent to select and call this tool correctly.
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 schema fully documents receipt_id with 100% coverage, so the baseline is 3. The description adds value by telling the agent where receipt_id comes from: the receipt_id or verify_url field on a verdict, or from a receipt the user was handed. That helps the agent find the actual value to pass.
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 opens with a specific verb and resource: 'Independently verify an OilFlow screening receipt.' It clearly distinguishes this from the sibling screening tools by explaining that it verifies receipts rather than performing new screening checks, and it describes the concrete outputs (receipt, canonical payload string, HMAC-SHA256 signature).
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 gives an explicit use case: use this whenever a user has been handed a compliance receipt and wants to know whether it is genuine and what it covered. It does not explicitly name alternatives or state when not to use it, but the context is clear enough that an agent could route correctly.
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. Dates show when Glama detected each change.
v0.3.1cluster_checkclusters_listkyc_screenlc_validatepredeal_checkpredeal_previewregulatory_checkregulatory_countriesregulatory_productsrequest_sandbox_keyubo_graphubo_screenverify_receiptMost tools map cleanly to a distinct resource or workflow: pre-deal, KYC, regulatory matrix, cluster feed, LC, UBO, receipts, and keys. The only real ambiguity is predeal_preview vs predeal_check, which are the same core function split by API-key requirement, and cluster_check vs clusters_list, though names and descriptions differentiate lookup from browsing. Overall an agent can reliably select the right tool.
The dominant pattern is domain_operation: predeal_preview, regulatory_check, cluster_check, kyc_screen, lc_validate, ubo_screen. A few tools break the pattern with verb-first names like verify_receipt and request_sandbox_key, and noun-only names like regulatory_countries, regulatory_products, clusters_list, and ubo_graph. It is not perfectly uniform, but the naming is still predictable and readable.
13 tools is well within the ideal range for a compliance-focused API and each tool corresponds to a distinct endpoint or workflow. The set covers multiple compliance subdomains without feeling bloated, and the free/keyed pre-deal pair is intentional rather than redundant.
The major workflows are covered: pre-deal checks, receipt verification, regulatory matrix lookups, cluster intelligence, LC validation, and UBO screening with polling. A minor gap is that kyc_screen can return queued results with a poll_url but there is no dedicated MCP tool to poll KYC results, though agents can work around this via the URL.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Sanctions screening, KYB, identifier validation, jurisdiction risk & secret scanning for AI agents
Entity verification, sanctions screening, and trust scoring for AI agents.
EU compliance checks for AI agents: sanctions, company, VAT ID, IBAN, email. Pay per call.
Compliance MCP for AI agents: sanctions & KYT screening on 50+ chains, stablecoin-freeze, oracle.
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