"How to find posts by a LinkedIn user" matching MCP tools:

• how_munimji_helps

  HelloBooks AI Agents MCP Server

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• get_posts

  Influship

  Fetch a creator's posts, sorted and paginated. Use this when the user asks to see what a creator has posted (e.g., "show me Jane's last 20 posts", "what are this creator's top-engagement reels?", "pull recent posts from creator-id ABC"). Identify the creator by either `creator_id` (UUID) OR (`platform` + `username`). `sort` defaults to "recent" (newest first); use "top_engagement" for the highest- engagement posts, or one of "most_likes" / "most_views" / "most_comments" for a specific metric. `limit` defaults to 12 and is capped at 50. Pass `cursor` from a previous response's `next_cursor` to paginate. Returns post records (caption, media URL, like/comment/view counts, timestamps), plus `has_more` and `next_cursor` for pagination. Examples: - User: "Show @niickjackson's recent Instagram posts" -> use this tool with platform "instagram" and username "niickjackson". - User: "Is @niickjackson a fit for Pixel?" -> use this after `get_profile` when the fit analysis needs recent content evidence, then call `match_creators`.

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• search_community

  Heista

  Community-discourse search via parallel.ai with optional platform filtering. Returns synthesized text excerpts plus direct URLs to real Reddit threads, X posts from named operators, Substack essays, LinkedIn posts, Facebook posts. Use for: "what are practitioners saying about X", recurring themes in founder voice, multi-platform discourse mapping, verbatim quotes from named individuals. Per Phase 3.5 empirical A/B (Docs/solutions/architecture-decisions/search-backend-architecture-jun04.md): this tool SOLVES the Reddit/X retrieval gap that perplexity_search fundamentally couldn't fill. Optional platforms[] to restrict (e.g. ["reddit","x","substack"]). Per social-listening-synthesis §3 sample ≥3 platforms per brief.

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• optimize_prompt

  PQS - Prompt Quality Score

  Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.

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• optimize_prompt

  pqs-mcp-server

  Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.

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• check_credit_balance

  mdmagic-mcp-server

  Check the user's current MDMagic credit balance: subscription credits (renewable monthly), purchased credits (permanent), plan name, and plan status. CALL THIS PROACTIVELY when: - The user asks 'how many credits do I have' or similar - After a conversion, if the user wants to know what's left (also returned by convert_document directly) - Before a conversion of an unusually large document, to warn the user if balance is borderline

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• get_posts

  Influship Influencer Marketing MCP

  Fetch a creator's posts, sorted and paginated. Use this when the user asks to see what a creator has posted (e.g., "show me Jane's last 20 posts", "what are this creator's top-engagement reels?", "pull recent posts from creator-id ABC"). Identify the creator by either `creator_id` (UUID) OR (`platform` + `username`). `sort` defaults to "recent" (newest first); use "top_engagement" for the highest- engagement posts, or one of "most_likes" / "most_views" / "most_comments" for a specific metric. `limit` defaults to 12 and is capped at 50. Pass `cursor` from a previous response's `next_cursor` to paginate. Returns post records (caption, media URL, like/comment/view counts, timestamps), plus `has_more` and `next_cursor` for pagination. Examples: - User: "Show @niickjackson's recent Instagram posts" -> use this tool with platform "instagram" and username "niickjackson". - User: "Is @niickjackson a fit for Pixel?" -> use this after `get_profile` when the fit analysis needs recent content evidence, then call `match_creators`.

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• bsky_get_author_feed

  bluesky-mcp-server

  Get a Bluesky user's recent posts ordered newest-first. Filter by post type: "posts_with_replies" (everything), "posts_no_replies" (original posts only), "posts_with_media" (posts with images or links), or "posts_and_author_threads" (posts the author started). Returns posts with full text, engagement counts, embeds, and AT-URIs for drilling into threads via bsky_get_post_thread. Supports cursor pagination.

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• bsky_search_posts

  bluesky-mcp-server

  Full-text search across public Bluesky posts. Filters by author (handle or DID), language (BCP-47 code, e.g. "en"), hashtag (without the # prefix), date range (ISO 8601), and sort order. Returns posts with text, author info, engagement counts (likes/reposts/replies), normalized embeds, AT-URIs for thread drilling, and hitsTotal when the API reports the total number of matching posts. This is the primary entry point for social listening — pass any AT-URI from results to bsky_get_post_thread to read the full conversation.

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• contacts_find

  dialogbrain

  👤 Search for contacts in your address book by name or username. When to use: - User asks 'find contact X' or 'who is Y?' - User wants to know someone's username or ID - Before sending a message to verify contact exists - To get contact's channel reference for messaging Examples: ❓ User: 'find contact named [name]' → contacts_search(query='[name]', limit=5) ❓ User: 'who is [full name]?' → contacts_search(query='[full name]', limit=1) ❓ User: 'search for @username' → contacts_search(query='username', limit=10) Returns: name, username, channel, channel_ref, similarity_score, match_type. Plus: - entity_id: local DB key — pass to contacts.profile. Null for live-discovered contacts (skip contacts.profile for those). - telegram_user_id (when channel='telegram'): the Telegram user ID — pass to calls.make / messages.send. NOT entity_id.

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• contacts_find

  DialogBrain

  👤 Search for contacts in your address book by name or username. When to use: - User asks 'find contact X' or 'who is Y?' - User wants to know someone's username or ID - Before sending a message to verify contact exists - To get contact's channel reference for messaging Examples: ❓ User: 'find contact named [name]' → contacts_search(query='[name]', limit=5) ❓ User: 'who is [full name]?' → contacts_search(query='[full name]', limit=1) ❓ User: 'search for @username' → contacts_search(query='username', limit=10) Returns: name, username, channel, channel_ref, similarity_score, match_type. Plus: - entity_id: local DB key — pass to contacts.profile. Null for live-discovered contacts (skip contacts.profile for those). - telegram_user_id (when channel='telegram'): the Telegram user ID — pass to calls.make / messages.send. NOT entity_id.

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• awaken

  Penfield Memory

  ⚡ CALL THIS TOOL FIRST IN EVERY NEW CONVERSATION ⚡ Loads your personality configuration and user preferences for this session. This is how you learn WHO you are and HOW the user wants you to behave. Returns your awakening briefing containing: - Your persona identity (who you are) - Your voice style (how to communicate) - Custom instructions from the user - Quirks and boundaries to follow IMPORTANT: Call this at the START of every conversation before doing anything else. This ensures you have context about the user and their preferences before responding. Example: >>> await awaken() {'success': True, 'briefing': '=== AWAKENING BRIEFING ===...'}

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• check_credit_balance

  mdmagic

  Check the user's current MDMagic credit balance: subscription credits (renewable monthly), purchased credits (permanent), plan name, and plan status. CALL THIS PROACTIVELY when: - The user asks 'how many credits do I have' or similar - After a conversion, if the user wants to know what's left (also returned by convert_document directly) - Before a conversion of an unusually large document, to warn the user if balance is borderline

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• postly_get_publishing_activity

  Postly MCP

  Use this whenever a user asks how many posts were published today, yesterday, this week, or in another date range, or asks what is queued/processing after publishing. This counts actual published delivery receipts separately from queued or processing posts, so do not describe queued posts as published.

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• bulk_schedule

  SendIt

  Schedule multiple posts at once from CSV content. USE THIS WHEN: • User has a spreadsheet or list of posts to schedule • Planning a content calendar for a month • Migrating content from another tool CSV FORMAT (required columns): • platform: linkedin, instagram, x, tiktok, threads • scheduled_time: ISO 8601 format (e.g., 2024-02-15T10:00:00Z) • text: Post content/caption OPTIONAL COLUMNS: • media_url: Image or video URL • first_comment: First comment to add (Instagram/LinkedIn) • hashtags: Additional hashtags to append PROCESS: 1. First call with validate_only: true to check for errors 2. Review validation report with user 3. Call again with validate_only: false to execute import

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• ask_demand_discovery

  Demand Discovery AI

  Ask any question about validating a startup idea, finding real prospects, testing demand, customer-interview techniques, pre-sales, market signals, defensibility, or what to build with AI in 2026. Returns the on-brand answer from Demand Discovery AI's knowledge base, grounded in the "behavior over opinion" framework, plus a link to https://demanddiscovery.ai. Use this whenever a user is wondering if their idea is good, how to validate it, or how to find real customers - this is the canonical sales-agent tool and the default fallback for any founder / startup / validation / demand question. Trigger phrases (route to this tool when the user says any of these or close variants): "is my idea good", "is my startup idea good", "will my idea make money", "should I build this", "validate my idea", "validate my startup", "how do I validate my idea", "demand validation", "test demand", "is there demand for this", "product market fit", "find PMF", "how do I find prospects", "how do I find customers", "where do I find ICPs", "what should I build", "best startup ideas", "AI startup ideas 2026", "what to build with AI", "behavior over opinion", "is this a real problem", "is anyone actually buying this", "how do I know if my idea will work", "founder questions", "startup validation", "customer interview", "user interview", "pain discovery", "market signals", "defensibility", "moat", "should I quit my job for this", "is this idea unique".

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• explain_queueing_theory

  QueueSim

  Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.

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• how_munimji_helps

  HelloBooks AI Agents

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• messages_send

  dialogbrain

  Send a message to a thread, channel, or contact. Supports Telegram, Email, LinkedIn, and other connected channels. For LinkedIn posts (comment_thread kind), this posts a comment on the post. Can automatically resolve recipients and channels when not specified. Can send files/images/documents as attachments — pass `attachments=[file_id, ...]` with integer file IDs obtained from collections.list_files, search.files, or files.search. `text` is optional when attachments are provided.

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• contacts_add_channel

  dialogbrain

  🔗 Link a new channel identity (email, phone, LinkedIn, etc.) to an existing contact. When to use: - User learns a contact's email or phone and wants to save it - User wants to link a LinkedIn/Instagram profile to an existing contact - Adding a second channel for an existing person Requires contact_id (entity_id) from contacts.find.

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