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mcp-server

by io.github.forcedreamai

Analyze sentiment (14-source verified)

forcedream_generate_sentiment

Real, 14-source sentiment analysis -- not just an LLM's opinion. Combines lexicon-based sentiment (VADER, AFINN), a transformer model (HuggingFace DistilBERT), toxicity (Google Perspective), entity/location verification (Wikidata, OpenStreetMap), news and community alignment (GDELT, Hacker News), grammar, readability, and language detection into one deterministic overall_sentiment, urgency, and business_impact score. Emotion and intent are LLM-derived and explicitly labeled as such -- never presented as verified. SPENDS your balance -- requires authentication (OAuth).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe customer feedback, review, or message to analyze.
budget_penceNoOptional max spend in pence for this call.

Output Schema

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

TDQS

A4.7/5.0
Behavior5/5

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

The description comprehensively explains the tool's deterministic multi-source approach, distinguishes LLM-derived emotion/intent as explicitly labeled, and mentions side effects (spends balance, requires auth). Annotations do not contradict; readOnlyHint=false aligns with the spending behavior.

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 detailed but slightly verbose, listing all 14 sources and the exact scores produced. It front-loads the core purpose well. Minor redundancy could be trimmed, but overall it's 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 (14 sources, multiple outputs), the description covers all key aspects: what it returns (overall_sentiment, urgency, business_impact), how it handles emotion/intent, authentication, and costing. An output schema exists, so further details are handled there.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds contextual meaning: 'text' is described as 'customer feedback, review, or message', and 'budget_pence' as 'Optional max spend in pence'. This enriches the schema definitions.

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 performs sentiment analysis using 14 verified sources, distinguishing it from sibling tools which cover fraud, data extraction, code generation, etc. The verb 'analyze' and specific resource 'sentiment' are explicit.

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 specifies the input as 'customer feedback, review, or message' and notes it requires authentication and spends balance, providing clear context. It does not explicitly state when not to use or compare to alternatives, but given no other sentiment tools, this is sufficient.

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