Skip to main content
Glama
524,660 tools. Updated 2026-09-06 17:35

"Indexing a codebase for accessible contextual queries" matching MCP tools:

  • Archive memories from the working or contextual tier into longterm. Entries are kept, not deleted; filters (keys, tags, from_tier, older_than_hours) combine as AND. Requires memory:write or full permission. Use delete_memory for irreversible removal, promote_memory to move a key the other way, and consolidate_memories when you also want a parent summary. Pass playbook_id as the UUID or GUID of the playbook this call should target.
    ConnectorNo auth
  • Execute a raw Overpass QL query for advanced spatial queries that the convenience tools do not cover. Use for multi-type queries, union queries, relation membership, historical queries, or any operation requiring full Overpass QL expressiveness. The query must include [out:json]. Example: "[out:json][timeout:15];node[\"natural\"=\"peak\"](47.5,-122.5,47.7,-122.2);out body;" Returns one page of the result set: use limit and offset to page through it, and read totalFound and truncated to see how much the query matched. Validate complex queries at overpass-turbo.eu before use. For simple "what's near X?" or "what's in this area?" queries, use openstreetmap_query_nearby or openstreetmap_query_bbox instead.
    ConnectorNo auth
  • Create or overwrite a memory entry by key. There is no separate update_memory; a second write to the same key replaces the previous value and cannot be undone. Use tier='working' for active tasks, 'contextual' for background context, 'longterm' for completed work. Set memory_type='hierarchical' and parent_key to build task graphs. Requires memory:write or full permission. Use delete_memory to remove a key, archive_memories to move it to longterm without deleting, and read_memory to fetch without replacing. Pass playbook_id as the UUID or GUID of the playbook this call should target.
    Connector
    Destructive
    No auth
  • Get a context-optimized view of memories: full working memory, summaries for contextual, and keys only for longterm. Read-only. Use this to pack a prompt; use read_memory for one key, search_memory to filter, and get_memory_tree for parent-child task graphs. Pass playbook_id as the UUID or GUID of the playbook this call should target.
    ConnectorNo auth
  • Trigger semantic indexing for a dataset — required before using dataset.chunks (Pro+ plan). Starts an async indexing job that splits the dataset into RAG-ready text chunks, generates embeddings, and stores them for semantic search. Indexing is idempotent: calling it again on an already-indexed dataset re-indexes with fresh embeddings. Indexing typically completes in 10–60 seconds depending on dataset size. After indexing, use dataset.chunks(dataset_id) to retrieve the text chunks. Args: dataset_id: ID of the built dataset to index (from job.status after dataset.build).
    ConnectorNo auth
  • Generate a contextual follow-up message that accounts for the full conversation history. Compares agent-provided messages with stored history to identify new messages, stores any new messages found, then uses AI to craft a reply. Also detects lead intent (interested, not_interested, asking_for_info, scheduling_call, other). Use for any message where prior conversation exists. For first messages, use campaignstack_craft_message.
    ConnectorAPI key

Matching MCP Servers

  • F
    license
    C
    quality
    D
    maintenance
    A Model Context Protocol server that provides Retrieval-Augmented Generation capabilities using Contextual AI, enabling AI interfaces like Cursor IDE and Claude Desktop to query domain-specific knowledge with context-aware responses and source citations.
    1
    21
    -
  • F
    license
    C
    quality
    D
    maintenance
    Provides RAG (Retrieval-Augmented Generation) capabilities via Contextual AI, enabling query processing and context-aware responses with citations. Integrates with MCP clients like Cursor IDE and Claude Desktop.
    1
    -

Matching MCP Connectors

  • Give your AI agent a phone. Place outbound calls to US businesses to ask, book, or confirm.

  • Ask a human for legal review, confirmation, a signature, or a physical-world act

  • Answer "is this network caught up?" with indexing freshness, lag, heads, and available tables. COMMON USER ASKS: - Is Base caught up? FIRST CHOICE FOR: - checking indexing head, lag, tables, and capabilities for one network WHEN TO USE: - You want to know whether a network is indexed, fresh, caught up, or behind before querying. - You need chain family, real-time status, or available tables for a network. DON'T USE: - You only need the latest block or slot number. EXAMPLES: - Is Base caught up?: {"network":"base-mainnet"}
    ConnectorNo auth
  • Check whether a merchant domain runs an ARC-compliant catalog (KaliCart Bridge). Returns bridge_version, merchant discovery URL and federated-indexing consent flags. A miss schedules a background probe. Use when you already know a merchant domain (verify ARC support, get its discovery URL); to discover products across merchants, use global_search.
    ConnectorNo auth
  • This is Anysearch's parallel search tool. Parallel search — run multiple Anysearch queries in a single call. Prefer this over multiple sequential calls when you have 2–5 queries. Saves context space and returns all results at once. Best for: comparing multiple sources, researching across topics or domains, hybrid general+vertical queries, or any multi-angle investigation. ## When to use Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries. 🏆 PRIMARY use case: After get_sub_domains(domains=[...]) returns sub_domains across multiple domains, use batch_search to send one query per sub_domain in parallel. This is more efficient than sequential per-domain search calls. Also useful for ambiguous / fuzzy queries within a single domain: after get_sub_domains, use batch_search to explore multiple sub_domains in parallel. ## Constraints - Maximum 5 queries per call - Each query item follows the search tool parameter structure (query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=<domain>) output — same rules as the search tool) - Queries run in parallel; a single query failure does not block others - REQUIRED PARAMS: Same rule as search — when a required param from get_sub_domains is not applicable, pass it as an empty string (key: ""). Never skip required params. ## Examples ### Single-domain batch (multiple sub_domains) Instead of: search(query="latest TSLA earnings", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA stock forecast", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA analyst rating", domain="finance", sub_domain="finance.us_stock") Use: batch_search(queries=[{query:"latest TSLA earnings", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA stock forecast", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA analyst rating", domain:"finance", sub_domain:"finance.us_stock"}]) ### Multi-domain batch (after get_sub_domains with multiple domains) After: get_sub_domains(domains=["finance", "health", "legal"]) Use: batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock", sub_domain_params:{ticker:"UNH"}}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}]) ### Hybrid: general + vertical in parallel (universal pattern for any borderline query) Use this whenever you are unsure if the query is pure encyclopedia or domain-specific — fire BOTH channels in batch_search: batch_search(queries=[ {query:"..."}, // general — no domain {query:"...", domain:"...", sub_domain:"..."}]) // vertical channel(s) This applies universally: classical texts, financial concepts, legal theories, historical events, scientific discoveries, medical topics — any query where domain knowledge could enrich the encyclopedia answer.
    Connector
    Destructive
    No auth
  • <tool_description> Check nDSG/GDPR/EU AI Act compliance status for a media buy. Verifies privacy-native architecture compliance. </tool_description> <when_to_use> Before activating a campaign or for compliance audits. Checks: no cookies, no fingerprinting, contextual targeting, data residency, revenue transparency, consent basis, agent transparency. </when_to_use> <combination_hints> create_media_buy → get_compliance_status → activate (if compliant). Use for regulatory reporting and audit trails. </combination_hints> <output_format> Overall compliance status + individual check results with details. </output_format>
    ConnectorNo auth
  • Given an active catalog brand name or merged alias, find similar brands using brand-profile vectors generated during product indexing. Unknown or ambiguous seeds return no brands. Returns up to 20 brands.
    ConnectorNo auth
  • List competitions accessible on your plan. Free tier: 12 majors. Use the returned `code` (e.g., "PL", "PD", "CL") for downstream calls.
    ConnectorNo auth
  • Start a NEW Echosaw analysis job from a publicly accessible media URL or video platform URL (YouTube, Rumble, Vimeo, etc.). This is an entry point that creates a job and begins processing — it does not fetch previously analyzed media (use echosaw_download_media for that). Returns a job ID (mediaId) used to track processing and retrieve results.
    ConnectorOAuth
  • Count PubMed publications by year for a biomedical topic. Use for publication momentum, emerging-target activity, or whether a field is accelerating or cooling. Returns exact PubMed search counts for up to 10 calendar years; volume can reflect indexing and terminology changes and is not evidence quality or commercial validation.
    ConnectorNo auth
  • Fetch a public business website page and return structured, accessible facts for a buyer-readiness review. It only examines the supplied public URL and does not scrape review platforms or private systems.
    ConnectorNo auth
  • Generate a contextual threaded reply for a LinkedIn comment. Fetches lead profile, campaign context, and ICP data from Convex, then uses AI to craft a relevant, conversational reply. When `addDmHint` is true (set when a DM is also being sent), the reply includes a 'Sent you a DM!' nudge. After calling this tool, the agent navigates to the post, finds the comment, clicks Reply, and types the returned `replyText` using browser_* tools.
    ConnectorAPI key
  • PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.
    ConnectorNo auth
  • Sends HTML to many displays at once. Target explicit display_ids, all=true for every accessible display, or include_category_ids to reach every display in those categories (include_descendants for subcategories, dry_run to preview matches without sending). Locked displays are skipped with reasons. For a single display use send_html. Requires content scope.
    Connector
    Destructive
    No auth
  • Get the graph schema for a locality (node types, relationships, sample queries). Call list_datasets first to get locality codes.
    ConnectorNo auth
  • Add an evidence bundle to a collection and trigger async vector indexing. Use after collection.create to populate a collection with documents. Once indexed, documents become searchable via collection.search and collection.ask. Indexing is async — poll job.status with the returned job_id until status is "complete". Also returns a signed action receipt (rcpt_...) binding this add call to the bundle manifest — list with receipt.list, verify with receipt.verify. PREREQUISITE: Bundle must have status "complete" (check with bundle.get). Collection must be owned by your API key. Returns: { collection_id, bundle_id, job_id (poll for indexing completion), receipt: ActionReceipt|null } Example prompts: - "Add my contract bundle ev_550e8400 to the Q4 Contracts collection." - "Put this evidence bundle into my Due Diligence Docs collection for search." - "Add document [bundle_id] to collection [col_id] with a title."
    ConnectorNo auth
  • Contextual market reads, grouped by `kind`. kind='regime'=market-regime labels (/v1/regime, market-wide, no symbol needed); 'phase'=move-lifecycle / entry-timing for a symbol (/v1/phase, premium+); 'derivatives'=normalized cross-exchange funding/OI/basis summary (/v1/derivatives); 'funding'=PER-VENUE funding+OI (/v1/funding); 'squeeze'=liquidation-cascade proximity (/v1/squeeze); 'intel'=per-symbol aggregated signal-quality roll-up (/v1/intel). Market DATA / context, NOT advice and NOT a win-rate. Empty/unknown kind → a menu of kinds.
    ConnectorNo auth