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mcp-server

by io.github.forcedreamai

Check IP / account fraud risk

forcedream_check_fraud
Read-only

Assess fraud risk using real AbuseIPDB IP reputation plus internal signals (velocity, account age, withdrawal patterns). Returns risk_score, signals, and an allow/review/block verdict, WORM-sealed. Requires authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ipNoOptional IP to check against AbuseIPDB.
actionYesAction being checked, e.g. "login", "withdrawal".
user_idYesThe user/account identifier to assess.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalsNo
verdictYes'allow', 'review', or 'block'.
worm_sealNo
risk_scoreYes
ip_reputationNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations provide readOnlyHint and openWorldHint. Description adds that the tool returns a WORM-sealed verdict, implying immutability, and mentions authentication needs. This goes beyond annotations without contradiction.

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 cover purpose, signals, outputs, and requirements with zero wasted words. Front-loaded with action and key details.

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 output schema exists and parameters are well-described, the description provides enough context for agent selection and invocation. It explains output structure and authentication, though additional detail on when to use over siblings could improve completeness.

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 coverage is 100% with per-parameter descriptions. The description adds context on how parameters are used together (AbuseIPDB, internal signals, output fields), enhancing meaning beyond the schema 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 uses specific verbs and resources: 'Assess fraud risk' with concrete signals (AbuseIPDB, velocity, account age, withdrawal patterns) and outputs (risk_score, signals, verdict). It clearly distinguishes from siblings like 'forcedream_security_scan' and 'forcedream_score_lead' by focusing on fraud risk assessment.

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 implicitly indicates usage for fraud risk assessment but does not explicitly state when to use versus alternatives or when not to use. It mentions requiring authentication, which is a precondition.

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

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TDQS

A4/5.0
Disambiguation3/5

Most tools have clearly distinct purposes (fraud vs extract vs generate vs sentiment vs lead scoring vs quote vs proof verification). However, there is notable overlap among the search_* discovery tools: forcedream_search_agents, forcedream_search_reliability, and forcedream_search_costs all surface overlapping agent metadata (success_rate appears in both search_agents and search_reliability), which could cause misselection. Additionally, forcedream_extract_data vs forcedream_extract_entities vs forcedream_extract_action_items overlap somewhat in the extraction domain despite distinct outputs (JSON fields vs raw entities vs action items).

Naming Consistency4/5

The forcedream_ prefix is used consistently throughout, and most tools follow a forcedream_<verb>_<object> pattern (extract_data, generate_code, score_lead, security_scan). However, there is inconsistency in verb style: check vs extract vs generate vs invoke vs search vs verify vs summarize are all different verb types, and the objects don't follow a uniform noun convention (some are actions like invole_agent, others resources like market_quote). The naming is readable and discoverable but not perfectly uniform.

Tool Count4/5

At 17 tools, this is slightly above the ideal range but justifiable given the broad multi-service scope (fraud, extraction, generation, discovery, verification). Each tool maps to a reasonably distinct service capability, and none feel like padding. The count borders on heavy but earns its place given the diverse domain coverage.

Completeness4/5

The tool surface is comprehensive for a multi-purpose AI/ML service platform, covering fraud detection, data extraction, code generation, sentiment analysis, embeddings, lead scoring, security scanning, summarization, market quotes, agent discovery, and proof verification. Missing are update/delete operations, but this appears to be a stateless service rather than a CRUD resource store. The discovery tools (search_* variants) and meta capabilities (verify_proof) round out the lifecycle well, though there's no clear cleanup or batch-processing tool.

Resources