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304,932 tools. Last updated 2026-07-22 03:17

"Cora" matching MCP tools:

  • Scan the tenant's seeded sessions with rule-based extractors (money, counts, dates, project-role, acquire, version-chain) and emit structured facts to the projection stream so they become queryable via enumerate_memory_facts. Use when enumerate_memory_facts returns insufficient rows for aggregation, version-chain, or money questions and you suspect the fact exists but was under-predicated at ingest. Idempotent — safe to re-run (duplicate fact_hashes skipped unless overwrite_existing=true). Profile 'comprehensive' runs all rule families; narrower profiles ('money', 'counts', 'dates', 'version_chains') target a single family. Returns facts_added + rules_matched + receipt_id. Gated by FACT_EXTRACTION_MODE on the server.
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  • Retrieve pre-synthesized per-session memory dossiers (typed: experience | fact | preference; with When/Involving/To-purpose metadata). Use for multi-session or preference-style questions where stitching across conversations is the bottleneck — the dossier already summarises each session's key events. Two modes: mode='search' with a query (BM25-ish ranking over summary+purpose, optional type_filter), or mode='list' returns the tenant's most-recent dossiers chronologically. Tenants without FEATURE_SESSION_DOSSIERS enabled return an empty list (no error).
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  • List all available Harvey Intel tools with pricing and input requirements. Use this for discovery.
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  • Return the calling agent's passport with current reputation tier and receipt count. Recalculates receipt count on every call and auto-upgrades the tier when new thresholds are met (basic 10+, established 100+, trusted 500+, elite 2000+). Includes a hint for the next tier upgrade.
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  • Task-scoped context briefing. Returns a prioritised context payload shaped by your task description, ranked by risk-if-missed. Constraints and alerts rank above general knowledge. Use at the START of reasoning about a question to get the system's best assessment of what's relevant. Complements query_memory: this gives breadth, query_memory gives depth.
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  • Contextual escalation — packages your full reasoning state (evidence gathered, options considered, recommended action) and routes to a human for review. Preserves work so the human responds with full context, not from scratch. Use when you hit genuine uncertainty that the system cannot evaluate.
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Matching MCP Servers

  • A
    license
    A
    quality
    F
    maintenance
    A lightweight short-term memory MCP server that automatically stores and recalls working context, session state, and task progress for AI agents. Memories auto-expire after 24 hours and integrate seamlessly with workspace-aware storage across multiple projects.
    Last updated
    10
    MIT

Matching MCP Connectors

  • Remote MCP server for OFAC screening, EDD memos, exposure forecasts, queues, and reports.

  • 15 Catalan portals + radio archive: gov, INE/REE/CNMC, CORA, Catalònica, radioteca.cat.

  • Composite server-side investigation tool. Pass a question and the server automatically: (1) detects intent (aggregation/temporal/ordering/knowledge-update/recall), (2) queries the entity index for structured facts, (3) builds a timeline for temporal questions, (4) retrieves memory chunks with the right scoring profile, (5) expands context around sparse hits, (6) derives counts/sums for aggregation, (7) assesses answerability, and (8) returns a recommendation. Use this as your FIRST tool for any non-trivial question — it does the multi-step investigation that would otherwise take 4-6 individual tool calls. The response includes structured facts, timeline, retrieved chunks, derived results, answerability assessment, and a recommendation for how to answer.
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  • List all available Zero Core Tools with pricing and input requirements. Use this for discovery.
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  • Receipted write-through to PlanCrux's log endpoint. Appends a structured log entry to a task with optional evidence references and stage binding. Cannot change task or stage status (human-only), but records work done, findings, and blockers encountered.
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  • What went wrong last time we touched this module? Returns past incidents, deploy failures, gotchas, and active constraints for a module or system. Use BEFORE modifying infrastructure code, deploy scripts, or any module with a history of fragility. Surfaces the kind of tribal knowledge that prevents repeat failures — Docker bind mount traps, Vault agent write patterns, stale dist/ artifacts, port conflicts, and similar operational landmines.
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  • Is it safe to deploy these changes? Cross-references your changed modules against active constraints, recent incidents, knowledge freshness, and active alerts. Returns a composite verdict (ready/caution/block) with per-module breakdown and actionable recommendations. Use BEFORE deploying to catch constraint violations, recent regressions in the same area, stale knowledge that needs verification, and active alerts that might interact with your changes.
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  • Record a simple pass/fail outcome report for a service call. No LLM analysis - just logs the result to the quality database. Cheaper alternative to verify_outcome when you only need to record success/failure.
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  • Find conflicting information across the user's memory. Returns groups of artefacts that contradict each other on the same topic. Use after gathering evidence for an answer — if your evidence sources disagree, this reveals which version is correct (typically the most recent).
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  • Reconstruct what the system knew at a specific point in time. Returns both current and superseded artefacts as of that timestamp. Use for temporal reasoning: 'what was true in January?' vs 'what is true now?' Compare two calls at different timestamps to see what changed.
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  • Create a receipted snapshot of your current decision state during a long-running session. Records decisions made, assumptions in effect, and open questions. Enables resumption by the same or different agent from the last checkpoint rather than replaying from zero.
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  • Retract a previously promoted skill. Sets the Engine artifact's living status to 'retracted', removing it from future retrieval results. Use when a skill is found to be incorrect or outdated.
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  • Cross-artefact-type changelog for specified domains since a given timestamp. Returns constraints added/updated, knowledge changes, decisions recorded, and alerts raised/resolved. Use at session start to learn what changed in your domain since your last session.
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  • Session-neighborhood expansion around promising retrieval hits. When you find a relevant chunk but the specific fact (name, date, amount) is in a nearby turn, use this to fetch ±N turns from the same session. Recovers facts like 'my parents', '$6', or 'Disney+' that are near but not in the retrieved chunk.
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  • Sufficiency gate — can this question be answered with current evidence? Pass your query and optionally the fact rows you have gathered. Returns: answerable (yes/no), missing fields, contradictory fields, recommended next tool, and confidence. Use this BEFORE forcing a best-guess answer. If answerable=false, it is better to say 'insufficient evidence' than to guess wrong.
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  • Why is this module built this way? Aggregates all architectural decisions, active constraints, corrections, and skills for a domain into a coherent narrative. Use BEFORE refactoring or questioning a design choice — the answer is often 'it's that way because of compliance/performance/incident X'. Returns decisions sorted by recency, active constraints that still apply, and correction history showing what was tried and reverted.
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