market-research
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Alternatives to market-research
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Related Servers
- AlicenseNot gradedqualityAmaintenanceMakes AI research agents accountable by giving every conclusion a traceable argument graph. Provides a persistent argument graph where claims require grounds and warrants for auditable, verifiable reasoning.2MIT
- AlicenseAqualityAmaintenanceEnables local AI models to structure and reason through debates using argument maps, with claims and supporting/attacking arguments automatically validated and organized.8AGPL 3.0
- AlicenseAqualityAmaintenanceEnables coding agents to analyze a repository evidence-first, answering questions as verifiable claims with source evidence, targeted test observations, and reference resolution.53Apache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to obtain structured, source-backed real-world evidence with provenance, freshness, conflicts, support levels, coverage, unresolved dependencies, and reusable integrity-checked receipts, including French company verification and beta import assessment.Apache 2.0
- AlicenseNot gradedqualityAmaintenanceProvides AI agents a shared long-term memory layer with evidence-backed, auditable claims, enabling persistent, explainable, and conflict-aware recall across sessions.1,551 PyPI6MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to create verifiable, replayable citations, search private knowledge bases, and publish Markdown with verified citation markers.MIT
TDQS
Scored across 47 tools
Most tools target distinct stages of the research pipeline, and descriptions clarify boundaries (e.g., lexical vs. semantic search, single vs. batch operations). However, the presence of legacy and batch variants (e.g., verify_claims vs. resolve_claims_batch) and multiple extraction submission paths introduces some ambiguity that an agent could misselect.
All tool names use snake_case with a clear verb_noun pattern (e.g., create_research, get_research_state, submit_extracted_signals). Batch variants consistently add a _batch suffix, and no camelCase or mixed conventions appear.
47 tools is far beyond the typical 3-15 range and exceeds the 25+ threshold for 'too many'. While the domain is complex, the sheer number increases cognitive load and risks tool selection errors.
The surface covers the full research lifecycle: creation, state management, stepping, extraction, verification, counterevidence, clustering, query lattice, source operations, search, comparison, analysis, and export. No obvious gaps in core workflows are apparent.