redflag-mcp
redflag-mcp
Servidor MCP que expone conocimientos sobre señales de alerta AML como herramientas consultables. Los oficiales de cumplimiento realizan preguntas en lenguaje natural; el servidor devuelve señales de alerta relevantes y con sus fuentes desde una base de datos vectorial local.
Descripción general
Tres flujos de trabajo distintos:
Extracción: extraer señales de alerta AML de archivos PDF o páginas web utilizando un LLM y guardarlas como YAML.
Ingesta: integrar (embed) los archivos YAML y cargarlos en la base de datos vectorial local.
Consulta: el servidor MCP responde a consultas de búsqueda semántica contra esa base de datos.
Related MCP server: Financial Intelligence MCP Server
Pipeline de extracción
scripts/extract.py toma un documento regulatorio (archivo PDF o URL), envía su texto a un modelo de OpenAI y escribe un archivo YAML estructurado en data/source/. Cada entrada extraída incluye una source_url que enlaza con el documento original.
Requisitos previos
uv sync --extra dev
export OPENAI_API_KEY=sk-...Agregar archivos PDF de forma masiva (flujo de trabajo recomendado)
Paso a paso:
Agregar la URL de origen a
red_flag_sources/pdflinks.txt: una URL por línea, en orden secuencial. Línea 1 → clave001, línea 2 →002, etc.Descargar el PDF y guardarlo en
red_flag_sources/pdf/con el nombreNNN_nombre_descriptivo_corto.pdf, dondeNNNcoincide con su posición de línea enpdflinks.txt.Regenerar el registro:
uv run python scripts/build_sources_registry.pyEjecutar la extracción:
uv run python scripts/extract.py --parallel
Restricción clave: el prefijo
NNN_en el nombre del archivo debe coincidir con el número de línea enpdflinks.txt. Línea 1 =001_*.pdf, línea 2 =002_*.pdf, etc. Así es como el extractor vincula cada PDF con su URL de origen pública.
Los archivos PDF se almacenan en red_flag_sources/pdf/ y deben nombrarse con un prefijo serial con ceros a la izquierda:
red_flag_sources/pdf/
001_fincen_alert_russian_sanctions_evasion.pdf
002_ffiec_bsa_aml_examination_manual.pdf
003_fatf_guidance_virtual_assets.pdfCada número de serie se asigna a una URL pública para el documento de origen. Mantenga este mapeo en red_flag_sources/pdflinks.txt — una URL por línea, en orden secuencial:
# 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/...Las líneas en blanco y las líneas que comienzan con # se ignoran. Después de editar pdflinks.txt, regenere sources.yaml:
uv run python scripts/build_sources_registry.pyLuego ejecute la extracción por lotes:
uv run python scripts/extract.py --parallelSolo se extraen los archivos PDF nuevos (no procesados); las fuentes procesadas anteriormente se omiten automáticamente.
Comandos de extracción por lotes
# 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 --parallelNota:
--rangese aplica solo a los archivos PDF numerados. Las URLs web enWeblinks.mdse excluyen cuando un rango está activo.
Fuente única (ad hoc)
# 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.pdfPara archivos PDF de fuente única, agregue la URL a pdflinks.txt y ejecute build_sources_registry.py primero para que el extractor pueda completar la source_url en la salida.
Qué hace
Obtiene el documento: descarga la página web (elimina navegación/pie de página/scripts) o lee el texto del PDF a través de pdfplumber.
Envía a OpenAI: solicita a
gpt-4o-mini(sobrescribir conOPENAI_EXTRACTION_MODEL) que extraiga cada indicador de señal de alerta AML distinto como JSON estructurado.Valida: cada señal devuelta se verifica contra el esquema
RedFlagSource; las entradas no válidas se omiten con una advertencia.Escribe YAML: guarda en
data/source/<slug>.yaml, una entrada por señal de alerta.Actualiza el manifiesto: registra la fuente en
data/source/.extracted_sources.yamlpara evitar el reprocesamiento.
Esquema de salida
Cada entrada en el archivo YAML tiene los siguientes campos:
Campo | Tipo | Requerido | Descripción |
| string | sí | Identificador único, ej. |
| string | sí | Descripción independiente del indicador de señal de alerta |
| string | no | URL pública del documento de origen |
| list[string] | no | Productos financieros a los que aplica (ej. |
| list[string] | no | Industrias o sectores de clientes a los que aplica (ej. |
| list[string] | no | Arquetipos de clientes a los que aplica (ej. |
| list[string] | no | Geografías o corredores relevantes (ej. |
| string | no | Nombre del documento de origen o autoridad (ej. |
| string | no |
|
| string | no | Tipología AML (ej. |
| string | no | Código de complejidad de simulación opcional (ej. |
Deduplicación
data/source/.extracted_sources.yaml rastrea cada fuente procesada por su ruta canónica o URL. Las fuentes que ya están en el manifiesto se omiten tanto en el modo por lotes como en el de fuente única. Use --force para volver a extraer una fuente independientemente.
Ingesta
Después de la extracción, integre los archivos YAML y cárguelos en la base de datos vectorial:
uv run python scripts/ingest.pyPara el corpus local inicial, ingiera solo los tres archivos objetivo:
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.yamlEsto genera embeddings con nomic-embed-text-v1.5 y realiza upsert de registros en LanceDB en data/vectors/. Ejecute la ingesta antes de conectar el servidor MCP a un cliente de escritorio; el modelo de embedding se descarga en el primer uso y se almacena mejor en caché durante la ingesta que durante el inicio del servidor.
OPENAI_API_KEY es opcional para la ingesta. Cuando está configurada, la ingesta puede etiquetar automáticamente metadatos faltantes en los registros derivados de LanceDB. Cuando no está configurada, la ingesta conserva los metadatos YAML disponibles y deja vacíos los campos de consulta enriquecidos faltantes. Los archivos YAML de origen no se sobrescriben mediante la ingesta.
Servidor MCP
# 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_mcpEl servidor expone tres herramientas: search_red_flags, get_red_flag y list_filters. Está completamente fuera de línea después de la ingesta; no se requieren claves API en el momento de la consulta.
Uso desde Codex
Para hilos locales de Codex, prefiera stdio para que Codex inicie el servidor MCP automáticamente:
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'Verifique el registro:
codex mcp list
codex mcp get redflag-mcpLuego inicie un nuevo hilo de Codex y solicite el servidor por su nombre, por ejemplo:
Use the redflag-mcp MCP server. List the available AML red flag filters.Si ya tiene el servidor HTTP ejecutándose, puede registrar ese en su lugar:
codex mcp add redflag-mcp-http --url http://127.0.0.1:8000/mcpVerificaciones locales (smoke checks)
Después de ingerir los tres archivos objetivo, verifique las herramientas con:
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")Para una consulta vaga como "¿qué debo buscar en las cuentas comerciales?", el agente que realiza la llamada primero debe hacer una breve pregunta de consulta que cubra el producto/canal, la industria, el perfil del cliente, la geografía y el canal o volumen de transacciones. Para una consulta específica, debe buscar directamente.
Desarrollo
uv sync --extra dev # Install dependencies
uv run pytest tests/ # Run tests
uv run ruff check src/ # Lint
uv run mypy src/ # Type checkAvailable Tools
7 toolsclassify_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.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| category | No | ||
| subjects | No | ||
| risk_level | No | ||
| product_types | No | ||
| industry_types | No | ||
| industry_groups | No | ||
| customer_profiles | No | ||
| geographic_footprints | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| cursor | No | ||
| detail | No | full | |
| category | No | ||
| subjects | No | ||
| regulator | No | ||
| source_id | No | ||
| risk_level | No | ||
| source_url | No | ||
| issued_after | No | ||
| issued_before | No | ||
| product_types | No | ||
| industry_types | No | ||
| industry_groups | No | ||
| typology_family | No | ||
| customer_profiles | No | ||
| regulatory_source | No | ||
| transaction_patterns | No | ||
| geographic_footprints | No | ||
| regulator_jurisdiction | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| red_flag_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| category | No | ||
| subjects | No | ||
| risk_level | No | ||
| product_types | No | ||
| industry_types | No | ||
| industry_groups | No | ||
| customer_profiles | No | ||
| geographic_footprints | No | ||
| regulator_jurisdiction | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
7 tool updates
v0.1.0- First observed
classify_red_flag_request - First observed
filter_red_flags - First observed
get_red_flag - First observed
get_source - First observed
list_filters - First observed
list_sources - First observed
search_red_flags
TDQS
Scored across 7 tools
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.
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_.
7 tools is well-scoped for the AML red flag domain, covering classification, filtering, retrieval, and metadata browsing without unnecessary bloat.
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.
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