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mcp-server

by io.github.forcedreamai

Score a sales lead

forcedream_score_lead

Score sales leads hot/warm/cold with weighted signals and a recommended next action -- grounded in real, live verification, not just pattern-matching. Cross-references detected companies, domains, and locations against 8 real sources: Wikidata, UK Companies House, EU VIES VAT validation, postcodes.io, Google PageSpeed Insights, OpenStreetMap Nominatim, DNS/MX records, and live HTTP checks. Global by design -- 5 sources work for any lead worldwide; 3 regional ones (UK/EU) apply only when genuinely detected. SPENDS your balance -- requires authentication (OAuth). Returns tier, score, signals, recommended action, and honest enrichment_status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
budget_penceNoOptional max spend in pence for this scoring call.
lead_descriptionYesFree-text description of the lead: company, contact, context, any details you have.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
outputNo
statusYes'completed' or 'error'.
verifyNo
task_idNo
proof_idNo
balance_penceNo
charged_penceNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, and the description adds important behavioral details: it spends balance, requires OAuth, and performs live verification against 8 sources. No contradictions with annotations.

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 adds value, front-loading the core purpose and then detailing sources and outputs. It could be slightly more concise but is well-structured.

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

Completeness5/5

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

Given the tool's complexity (8 sources, global/regional, multiple outputs), the description is comprehensive. It covers return fields, warns about spending, and explains source behavior, leaving no major gaps.

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 coverage is 100% with clear descriptions for both parameters. The description adds no new semantics beyond 'free-text description' and 'optional max spend', so a baseline score of 3 is appropriate.

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 ('Score sales leads hot/warm/cold') and clearly states the resource ('sales leads') and outputs (tier, score, signals, etc.). It distinguishes from sibling tools like forcedream_check_fraud and forcedream_extract_data by focusing on lead scoring.

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 implies usage for scoring any sales lead and mentions costs and authentication, but does not explicitly compare with siblings or state when not to use it. However, the global/regional sourcing note provides some usage context.

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