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redflag-mcp

MCP 服务器,将反洗钱 (AML) 红旗知识作为可查询工具公开。合规官提出自然语言问题;服务器从本地向量数据库返回相关的、有来源的红旗信息。

概述

三个不同的工作流程:

  1. 提取 — 使用 LLM 从 PDF 或网页中提取 AML 红旗信息,并将其保存为 YAML

  2. 摄入 — 对 YAML 文件进行向量化并加载到本地向量数据库中

  3. 查询 — MCP 服务器针对该数据库回答语义搜索查询


Related MCP server: Financial Intelligence MCP Server

提取流水线

scripts/extract.py 获取监管文档(PDF 文件或 URL),将其文本发送给 OpenAI 模型,并将结构化的 YAML 文件写入 data/source/。每个提取的条目都包含一个指向原始文档的 source_url

先决条件

uv sync --extra dev
export OPENAI_API_KEY=sk-...

批量添加 PDF(推荐工作流程)

分步指南:

  1. 将源 URL 添加到 red_flag_sources/pdflinks.txt — 每行一个 URL,按顺序排列。第 1 行 → 键 001,第 2 行 → 002,依此类推。

  2. 下载 PDF 并将其保存到 red_flag_sources/pdf/,命名为 NNN_short_descriptive_name.pdf,其中 NNN 与其在 pdflinks.txt 中的行位置匹配。

  3. 重新生成注册表: uv run python scripts/build_sources_registry.py

  4. 运行提取: uv run python scripts/extract.py --parallel

关键约束: 文件名中的 NNN_ 前缀必须与 pdflinks.txt 中的行号匹配。第 1 行 = 001_*.pdf,第 2 行 = 002_*.pdf,依此类推。这是提取器将每个 PDF 链接到其公共源 URL 的方式。


PDF 存储在 red_flag_sources/pdf/ 中,必须使用零填充的序列前缀命名:

red_flag_sources/pdf/
  001_fincen_alert_russian_sanctions_evasion.pdf
  002_ffiec_bsa_aml_examination_manual.pdf
  003_fatf_guidance_virtual_assets.pdf

每个序列号映射到源文档的公共 URL。在 red_flag_sources/pdflinks.txt 中维护此映射 — 每行一个 URL,按序列顺序排列:

# FinCEN Russian Sanctions Evasion Alert
https://fincen.gov/sites/default/files/2022-06/Alert%20FIN-2022-Alert001_508C.pdf

# FFIEC BSA/AML Examination Manual
https://bsaaml.ffiec.gov/manual

# FATF Guidance on Virtual Assets
https://www.fatf-gafi.org/...

空行和以 # 开头的行将被忽略。编辑 pdflinks.txt 后,重新生成 sources.yaml

uv run python scripts/build_sources_registry.py

然后运行批量提取:

uv run python scripts/extract.py --parallel

仅提取新的(未处理的)PDF — 之前处理过的源会自动跳过。

批量提取命令

# Sequential batch
uv run python scripts/extract.py

# Parallel batch (4 workers by default)
uv run python scripts/extract.py --parallel

# Parallel batch with custom worker count
uv run python scripts/extract.py --parallel 8

# Force re-extract everything
uv run python scripts/extract.py --force --parallel

# Process only PDFs in a serial range (e.g. 001 through 005)
uv run python scripts/extract.py --range 001-005

# Range + parallel
uv run python scripts/extract.py --range 001-005 --parallel

# Force re-extract a range
uv run python scripts/extract.py --force --range 001-005 --parallel

注意: --range 仅适用于编号的 PDF。当激活范围时,Weblinks.md 中的 Web URL 将被排除。

单个源(临时)

# Extract from a local PDF
uv run python scripts/extract.py red_flag_sources/pdf/001_fincen_alert.pdf

# Extract from a URL
uv run python scripts/extract.py https://example.com/regulatory-guidance

# Re-extract a source that was already processed
uv run python scripts/extract.py --force red_flag_sources/pdf/001_fincen_alert.pdf

对于单个源 PDF,请先将 URL 添加到 pdflinks.txt 并运行 build_sources_registry.py,以便提取器可以在输出中填充 source_url

功能说明

  1. 获取文档 — 下载网页(去除导航/页脚/脚本)或通过 pdfplumber 读取 PDF 中的文本

  2. 发送至 OpenAI — 提示 gpt-4o-mini(可通过 OPENAI_EXTRACTION_MODEL 覆盖)将每个不同的 AML 红旗指标提取为结构化 JSON

  3. 验证 — 根据 RedFlagSource 模式检查每个返回的红旗;无效条目将被跳过并发出警告

  4. 写入 YAML — 保存到 data/source/<slug>.yaml,每个红旗一个条目

  5. 更新清单 — 在 data/source/.extracted_sources.yaml 中记录源,以防止重复处理

输出模式

YAML 文件中的每个条目都有以下字段:

字段

类型

必需

描述

id

string

唯一标识符,例如 001-fincen-alert-01

description

string

红旗指标的独立描述

source_url

string

源文档的公共 URL

product_types

list[string]

适用的金融产品(例如 depository, crypto, msb

industry_types

list[string]

适用的客户行业或部门(例如 oil_and_gas, government_benefits

customer_profiles

list[string]

适用的客户原型(例如 small_business, charity_or_nonprofit

geographic_footprints

list[string]

相关的地理位置或走廊(例如 southwest_border, mexico

regulatory_source

string

源文档名称或权威机构(例如 FinCEN Alert FIN-2022-Alert001

risk_level

string

high, mediumlow

category

string

AML 类型学(例如 structuring, sanctions_evasion, shell_company

simulation_type

string

可选的模拟复杂性代码(例如 1A, 2B

去重

data/source/.extracted_sources.yaml 通过其规范路径或 URL 跟踪每个已处理的源。清单中已有的源在批量和单源模式下都会被跳过。使用 --force 强制重新提取源。


摄入

提取后,对 YAML 文件进行向量化并加载到向量数据库中:

uv run python scripts/ingest.py

对于初始本地语料库,仅摄入三个目标文件:

uv run python scripts/ingest.py \
  data/source/001_federal_child_nutrition_fraud.yaml \
  data/source/002_oil_smuggling_cartels.yaml \
  data/source/003_bulk_cash_smuggling_repatriation.yaml

这将使用 nomic-embed-text-v1.5 生成嵌入,并将记录插入到 data/vectors/ 的 LanceDB 中。在将 MCP 服务器连接到桌面客户端之前运行摄入;嵌入模型在首次使用时下载,在摄入期间比在服务器启动期间缓存效果更好。

OPENAI_API_KEY 对于摄入是可选的。设置后,摄入可以自动将缺失的元数据标记到派生的 LanceDB 记录中。未设置时,摄入将保留现有的 YAML 元数据,并使缺失的丰富咨询字段保持为空。源 YAML 文件不会被摄入过程重写。


MCP 服务器

# Start server (stdio mode, for Claude Desktop / Claude Code)
uv run python -m redflag_mcp

# Start in MCP inspector
uv run mcp dev src/redflag_mcp/server.py

# Start as HTTP server (for OpenAI agents or other HTTP clients)
MCP_TRANSPORT=http MCP_HOST=0.0.0.0 MCP_PORT=8000 uv run python -m redflag_mcp

服务器公开了三个工具:search_red_flagsget_red_flaglist_filters。摄入后它完全离线 — 查询时不需要 API 密钥。

从 Codex 使用

对于本地 Codex 线程,首选 stdio,以便 Codex 自动启动 MCP 服务器:

codex mcp add redflag-mcp -- zsh -lc 'cd /Users/learningmachine/Documents/Python-dev/redflag-mcp && HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 uv run python -m redflag_mcp'

验证注册:

codex mcp list
codex mcp get redflag-mcp

然后启动一个新的 Codex 线程并按名称请求服务器,例如:

Use the redflag-mcp MCP server. List the available AML red flag filters.

如果您已经运行了 HTTP 服务器,则可以改为注册该服务器:

codex mcp add redflag-mcp-http --url http://127.0.0.1:8000/mcp

本地冒烟测试

摄入三个目标文件后,使用以下命令验证工具:

list_filters
search_red_flags(query="federal child nutrition program sponsor receives reimbursements inconsistent with its profile", product_types=["depository"])
search_red_flags(query="southwest border oil company wires for waste oil or hazardous materials")
search_red_flags(query="bulk cash moved by armored car service to Mexico")
get_red_flag(red_flag_id="001_federal_child_nutrition_fraud-01")

对于诸如“我在商业账户中应该寻找什么?”之类的模糊查询,调用代理应首先提出一个简短的咨询问题,涵盖产品/渠道、行业、客户概况、地理位置以及交易渠道或金额。对于特定查询,它应直接搜索。


开发

uv sync --extra dev              # Install dependencies
uv run pytest tests/             # Run tests
uv run ruff check src/           # Lint
uv run mypy src/                 # Type check

Available Tools

7 tools
classify_red_flag_requestA

Classify an AML red flag request before searching when the user asks which red flags apply to a product, customer, geography, industry, scenario, transaction pattern, or institution profile. Returns one route: needs_more_context, metadata_filter, filtered_relevance_search, or direct_relevance_search, plus the recommended next tool and arguments. Use it for ambiguous 'what red flags apply' requests; skip it when the user already gives specific metadata filters or a concrete scenario.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
categoryNo
subjectsNo
risk_levelNo
product_typesNo
industry_typesNo
industry_groupsNo
customer_profilesNo
geographic_footprintsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It states the tool returns one route and recommended next tool, but lacks details on side effects, authentication requirements, rate limits, or idempotency. The read-only nature is implied but not confirmed.

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?

Two tightly written sentences: first for purpose, second for usage guidance. No redundant or superfluous information. Highly efficient.

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

Completeness3/5

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

Given 10 parameters, no schema coverage, and an output schema (not shown), the description provides purpose and usage but omits parameter semantics and behavioral nuances. It is moderately complete for a classification tool but leaves gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%; the description does not explain individual parameters beyond the general notion of query content. The many optional parameters (limit, category, subjects, etc.) are not described, forcing reliance on schema enums alone.

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: to classify ambiguous AML red flag requests and return a route with recommended next tool. It distinguishes from siblings by specifying when to use (ambiguous requests) and when to skip (specific filters or concrete scenario).

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?

Explicitly provides when-to-use and when-not-to-use guidelines: 'Use it for ambiguous requests; skip it when the user already gives specific metadata filters or a concrete scenario.' This is clear and actionable.

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

filter_red_flagsA

Return AML red flags for exact metadata criteria without ranked relevance search. Use this for exact metadata requests, broad investigative subjects, and broad industry groups, such as high-risk depository structuring red flags, FINTRAC human trafficking red flags with subjects, trade logistics red flags with industry_groups, or red flags from regulators in France. category is the primary record classification; subjects is a broader eligibility layer that catches cross-category flags; typology_family is a broader proceeds or typology grouping. For example, a human-trafficking-relevant darknet crypto flag can have category="virtual_currency" while matching subjects=["human_trafficking"]. Paginate with next_cursor whenever truncated is true; search_red_flags is ranked and limit-based, with no cursor. For country or jurisdiction requests, translate names to ISO-style regulator_jurisdiction codes before filtering: France -> FR, Singapore -> SG, Australia -> AU, United Kingdom/UK -> GB, United States/US -> US, and European Union/EU regulators -> EU. Prefer filter_red_flags(regulator_jurisdiction="FR") for requests like "red flags from regulators in France." regulator_jurisdiction describes issuer jurisdiction; geographic_footprints describes affected geography or typology geography. Use search_red_flags instead for open-ended relevance questions. Successful responses include table-ready display hints in display and a portable Markdown fallback in markdown_table; clients decide how to render them.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
cursorNo
detailNofull
categoryNo
subjectsNo
regulatorNo
source_idNo
risk_levelNo
source_urlNo
issued_afterNo
issued_beforeNo
product_typesNo
industry_typesNo
industry_groupsNo
typology_familyNo
customer_profilesNo
regulatory_sourceNo
transaction_patternsNo
geographic_footprintsNo
regulator_jurisdictionNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.5/5.0
Behavior5/5

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

Without annotations, the description carries full burden. It explains that the tool performs exact matching, not ranked search; defines how category, subjects, and typology_family interact; provides country code translation rules; and notes pagination. It also mentions response includes display hints and markdown_table. This is thorough behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively long but every sentence serves a purpose: purpose, usage, parameter explanations, pagination, country codes, alternative tool. It is well-structured and front-loaded with the core purpose. Slight wordiness in examples could be trimmed but overall effective.

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?

With 20 parameters, 0% schema coverage, and no annotations, the description covers the most critical aspects (core parameters, pagination, output format). It assumes an output schema exists, which handles return values. While not exhaustive for every parameter, it provides enough context for correct usage of the tool's main features.

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?

Given 0% schema description coverage, the description must compensate. It explains key parameters (category, subjects, typology_family, regulator_jurisdiction, geographic_footprints) with examples and contrasts. However, many parameters (limit, cursor, detail, regulator, source_id, risk_level, etc.) are not mentioned, leaving gaps. It adds value for the most important ones but is not complete.

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 opens with 'Return AML red flags for exact metadata criteria without ranked relevance search,' clearly stating the verb ('Return'), resource ('AML red flags'), and distinguishing it from the sibling tool search_red_flags. It is specific and leaves no ambiguity about what the tool does.

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 explicitly provides usage scenarios ('Use this for exact metadata requests...') and gives concrete examples (e.g., 'high-risk depository structuring red flags'). It also states when to use the alternative tool ('Use search_red_flags instead for open-ended relevance questions') and covers pagination behavior with next_cursor. This is comprehensive guidance.

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

get_red_flagB

Return one AML red flag by id, including source and citation metadata.

ParametersJSON Schema
NameRequiredDescriptionDefault
red_flag_idYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

B3.3/5.0
Behavior2/5

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

No annotations provided, so description carries full burden. It mentions returning source and citation metadata but does not disclose authentication needs, rate limits, or error behavior (e.g., missing ID).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, highly concise, no wasted words. Front-loaded with the core action.

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

Completeness3/5

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

With output schema present, return values are covered. However, missing guidance on usage, potential errors, and what 'source and citation metadata' entails reduces completeness for a retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. It only adds 'by id' but provides no format or constraints for the red_flag_id parameter, missing an opportunity to clarify value semantics.

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?

Description clearly states it returns a single AML red flag by ID, including source and citation metadata. It distinguishes from sibling tools like search_red_flags and filter_red_flags.

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

Usage Guidelines3/5

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

No explicit when to use vs alternatives. The description implies use for fetching one specific flag by ID, but no guidance on not using it for multiple flags or which sibling to use instead.

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

get_sourceA

Return bounded detail for one source by source_id, including citations, aggregate metadata, related red flag IDs, and short snippets. Use get_red_flag when full text for one red flag is needed.

ParametersJSON Schema
NameRequiredDescriptionDefault
source_idYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so description carries the burden. Mentions 'bounded detail' but does not explain what 'bounded' means operationally. Does not explicitly state read-only behavior or side effects, though implied by 'return'.

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?

Two sentences, front-loaded with action and key details. No unnecessary words. Efficient and easy to parse.

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 output schema exists, return values need not be fully described. Covers key output elements and sibling distinction. Lacks error handling or precondition notes, but acceptable for a simple retrieval tool.

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 has one parameter (source_id) with 0% coverage. Description references source_id but adds no further semantics (e.g., format, source, or how to obtain valid IDs). Adds minimal value beyond the parameter name.

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?

Clearly states the verb 'Return' and resource 'one source by source_id'. Lists included elements (citations, metadata, red flag IDs, snippets) and distinguishes from sibling get_red_flag.

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?

Explicitly directs when to use get_red_flag instead for full text, providing a clear alternative. Does not address other siblings or general when-not-to-use scenarios, but sufficient for the simple context.

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

list_filtersA

List available filter values for product_types, industry_types, industry_groups, customer_profiles, geographic_footprints, subjects, typology_family, transaction_patterns, category, risk_level, regulator, and regulator_jurisdiction. Agents should call this before or during consultation when they need valid local filter values. category is the primary record classification; subjects is the broad investigative eligibility layer; typology_family is a broader proceeds or typology grouping. regulator_jurisdiction describes issuer jurisdiction; geographic_footprints describes affected geography or typology geography.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden. It explains that the tool returns available filter values and adds semantic clarifications for several fields (e.g., category, subjects, typology_family). This provides useful behavioral context beyond a simple listing.

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 concise with three sentences: first states the action, second gives usage guidance, third clarifies key field semantics. No wasted words.

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 has no parameters and an output schema exists, the description adequately explains the purpose and some field meanings. It could be more complete by describing the expected output format or mentioning that the output is a list of valid values.

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 tool has no parameters, so the schema coverage is complete. The description adds value by explaining the meaning of the fields that will appear in the output, which compensates for the lack of parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists available filter values for a specific set of fields. While it distinguishes from sibling tools implicitly (sibling tools focus on red flags and sources), it does not explicitly differentiate itself.

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 explicitly says to call this before or during consultation when valid local filter values are needed. It provides clear context but does not mention when not to use it or suggest alternatives.

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

list_sourcesA

List ingested AML red flag source coverage with citation URLs, source counts, aggregate metadata, and red flag IDs. Use when users ask what sources or citations the corpus covers.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.1/5.0
Behavior2/5

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

No annotations provided, and the description does not disclose any behavioral traits (e.g., side effects, authorization, rate limits). For a read-only list tool, the omission is a minor gap but still reduces transparency.

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?

Two concise sentences with no filler; front-loads the purpose and usage guidance.

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?

Has output schema (not shown) and description lists key elements returned. For a zero-parameter list tool with rich output schema, completeness is high.

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?

Zero parameters (schema coverage 100%), so baseline is 4. No additional parameter meaning needed.

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 explicitly states the tool lists AML red flag source coverage with specific outputs (citation URLs, source counts, aggregate metadata, red flag IDs), and differs from sibling tools like get_source (single source) and search_red_flags.

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?

Includes explicit when-to-use instruction: 'Use when users ask what sources or citations the corpus covers.' Does not specify when not to use, but the context is clear given siblings.

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

search_red_flagsA

Search AML red flags using natural-language context and optional filters.

Successful responses include table-ready display hints in display and a portable Markdown fallback in markdown_table; clients decide how to render them.

Agent guidance: use classify_red_flag_request before searching for ambiguous "what red flags apply" requests; skip that extra call when the user already gives specific metadata filters or a concrete scenario. If the user's request is vague, briefly ask for product/channel, industry, customer profile, geography, and transaction channel or volume before searching. If the request already names those details or has a specific scenario, search directly. Call list_filters when you need valid filter values. Use filter_red_flags for exact metadata requests and exhaustive enumeration; use search_red_flags for ranked relevance questions and increase limit for more ranked results because search has no cursor. For broad investigative topics such as human trafficking red flags, use subjects as an eligibility filter. Category is the primary record classification; subjects is a broader eligibility layer that catches cross-category flags; typology_family is a broader proceeds or typology grouping. For example, a human-trafficking-relevant darknet crypto flag can have category="virtual_currency" while matching subjects=["human_trafficking"]. For broad sector requests such as trade logistics red flags, use industry_groups as an eligibility filter. regulator_jurisdiction describes issuer jurisdiction; geographic_footprints describes affected geography or typology geography. For country or jurisdiction requests about issuing regulators, translate names to regulator_jurisdiction codes before filtering, such as France -> FR, Singapore -> SG, Australia -> AU, United Kingdom/UK -> GB, United States/US -> US, and European Union/EU regulators -> EU.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
categoryNo
subjectsNo
risk_levelNo
product_typesNo
industry_typesNo
industry_groupsNo
customer_profilesNo
geographic_footprintsNo
regulator_jurisdictionNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.8/5.0
Behavior5/5

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

No annotations provided, so description fully carries the burden. Discloses response format (display hints, markdown table), lack of cursor (increase limit for more results), and explains semantics of key filters like category, subjects, typology_family, regulator_jurisdiction, geographic_footprints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Relatively long (~300 words) but well-structured with clear sections: purpose, response format, agent guidance. Each sentence adds value; no fluff. Slightly verbose but efficient.

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?

Thorough guidance for a tool with 11 parameters, no schema descriptions, and 7 siblings. Covers usage scenarios, parameter semantics, response details, and even country code mappings. Fully compensates for missing schema descriptions.

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 0%, but description adds significant semantics: explains difference between category, subjects, and typology_family; clarifies regulator_jurisdiction vs geographic_footprints; gives examples of country code translations. Does not cover every parameter individually but compensates well.

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?

Clearly states the tool searches AML red flags using natural-language context and optional filters. Differentiates from siblings like classify_red_flag_request, filter_red_flags, and list_filters by specifying distinct use cases.

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?

Provides explicit guidance on when to use this tool vs alternatives: use classify_red_flag_request for ambiguous requests, skip for specific metadata filters; use filter_red_flags for exact metadata; use list_filters for valid values. Also advises on handling vague requests.

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.

  1. 7 tool updatesv0.1.0
    • First observedclassify_red_flag_request
    • First observedfilter_red_flags
    • First observedget_red_flag
    • First observedget_source
    • First observedlist_filters
    • First observedlist_sources
    • First observedsearch_red_flags

TDQS

A4.2/5.0

Scored across 7 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: classification, exact filtering, single retrieval, source retrieval, listing filters, listing sources, and semantic search. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (classify_red_flag_request, filter_red_flags, etc.), with predictable verbs like get_, list_, search_.

Tool Count5/5

7 tools is well-scoped for the AML red flag domain, covering classification, filtering, retrieval, and metadata browsing without unnecessary bloat.

Completeness5/5

The tool set provides a complete read-only surface: routing ambiguous requests, exact filtering, single record lookup, source detail, filter values, source coverage, and full-text search. No obvious gaps.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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