Reddit Research MCP vs Veezee MCP

linkedin_get_profile

Fetch one person's LinkedIn profile. identifier accepts a profile URL, the slug after /in/ (e.g. 'williamhgates'), or a urn:li:fsd_profile URN; URLs are cleaned automatically. Always returns the overview (name, headline, location, current position, follower counts) plus up to 2 requested sections from about|experience|education|skills at no extra cost; each section beyond 2 adds 2 credits (max 4 sections). Costs 4 credits base. If you only have a name, use linkedin_search_people first; this tool does not search. Results from linkedin_search_people with is_anonymous=true cannot be fetched here; treat them as 'someone matching this exists' and stop. Companies belong to linkedin_get_company.

linkedin_search_people

Find people on LinkedIn by keywords and filters. The right tool when you have a name, role, or 'who is the X at Y' question without a profile URL. Pass keywords (free text: name, title, or both) and any of first_name, last_name, title, school, current_company, past_company. If keywords is omitted it is derived from the name or title filters; school or company filters alone are rejected with INVALID_INPUT, so include keywords with those. Company filters accept a company name, slug, numeric id, or URN. current_company names are matched by LinkedIn's own company search: typo-tolerant and fuzzy, so results can include people whose headline merely mentions the company; when you need exact filtering, pass the numeric id or URN (linkedin_get_company returns both). past_company names are resolved to an id for you. Costs 10 credits including the first 10 results; each further 10 results add 1 credit (limit max 30; trial keys max 10). A cursor page is a NEW call priced the same way by its own limit, so one limit=30 call is much cheaper than three limit=10 pages; prefer a larger limit over paginating. Do NOT combine a past_company NAME or a company-URL filter with limit=30: resolving those spends one of the call's three internal fetches, so that combination is rejected; keep limit<=20 with them or pass the numeric id. current_company names never spend a fetch, so they combine with any limit. Returns name, position, location, urn, public_identifier per result, a cursor for the next page, and total_matches. Results with is_anonymous=true are private profiles; do not pass them to linkedin_get_profile. For one known person with a URL/slug, call linkedin_get_profile directly instead.

linkedin_get_company

Fetch one company's LinkedIn page: name, description, industry, employee count, headquarters, website, founding year, specialities, and the URN/numeric id you need for linkedin_search_people company filters. identifier accepts a company URL, the slug after /company/ (e.g. 'microsoft'), or a website domain like 'microsoft.com'; numeric ids and URNs are search-filter inputs, not fetch identifiers. Domains are resolved to a company and verified against that company's website: a domain identifier always QUOTES base+4 credits (set max_credits accordingly), and the 4-credit resolution surcharge is refunded at settlement when the domain was resolved before, so known domains settle at the base price. A domain that cannot be verified to a company returns INVALID_INPUT with the closest matches instead of a guessed company. Costs 4 credits base. Do not guess a slug from a brand name: slugs are vanity strings and a famous name can belong to an unrelated company's page (linkedin.com/company/anthropic is a small investment fund, not the AI lab). When you only know the company's name, pass its website domain instead -- the verified form -- and sanity-check the returned industry and description against what you expected. This tool does not search by name: if you only have an approximate company name, use linkedin_search_people's current_company filter with keywords (names are matched natively there) or give the exact slug. For the company's posts, use linkedin_get_posts with the same identifier (URL, slug, or website domain all work there too).

linkedin_get_posts

Fetch the recent LinkedIn posts of one person or one company. identifier accepts a profile or company URL, a slug, a person URN, or a company website domain like 'microsoft.com'; the entity type is detected automatically. A domain resolves to its verified company first, exactly like linkedin_get_company: it QUOTES base+4 credits (set max_credits accordingly) and the surcharge is refunded at settlement for already-known domains, so they settle at the base price. Company URNs and numeric company ids are search-filter inputs, not fetch identifiers: use the company slug, URL, or domain here. Returns one page of posts (text, created_at, author, likes, comments_count, shares, is_repost, url) with a cursor for older posts. Costs 4 credits per page. Use this for 'what has X been posting', voice-of-company research, or activity checks before outreach. Not for reading one specific post you already have a URL for, and not for keyword search across LinkedIn; neither is supported in v1.

reddit_search

Search all of Reddit by keywords. type picks the target: posts (default), comments (what people actually say about a product, problem, or brand -- unique to Reddit search), subreddits (find communities), users. Pass query as free text up to 256 characters; decompose broad topics into several narrower queries, since result depth per query is capped upstream around a few hundred results. sort applies to posts (relevance|top|new|hot|comment_count) and comments (relevance|top|new); range (past_hour..all_time) applies to posts only; both are rejected with INVALID_INPUT elsewhere. Costs 6 credits per page; a cursor page is a NEW call priced the same way. Returns one page of summaries (author, title or comment text, upvotes, comment_count, created_at, permalink, id) with a cursor; there is no server-side time window on comment search, so for monitoring filter on created_at yourself and poll with sort=new. Fetch full post bodies and discussion threads with reddit_get_post; read one community's feed with reddit_get_subreddit_posts. For high-volume recency sweeps across X instead, use x_search.

reddit_get_subreddit

Fetch one subreddit's profile: title, description, subscriber and active-user counts, age, NSFW flag, and topics. subreddit_name is the name without the r/ prefix, e.g. 'selfhosted'; a full reddit.com URL also works. Set include_settings to also get the community rules and moderator list for +2 credits (useful before posting or judging moderation culture). Costs 4 credits base. Subscriber counts here are the standard sizing signal for market research. This tool does not return posts: read the feed with reddit_get_subreddit_posts, and discover subreddits you don't know by name with reddit_search type=subreddits.

reddit_get_subreddit_posts

Fetch one page of posts from a single subreddit, the community-monitoring primitive. subreddit_name is the name without the r/ prefix. sort defaults to the subreddit's own front-page order (best); use sort=new for monitoring. sort=top and controversial need a range, but the window is currently not applied upstream on this feed: top returns the subreddit's all-time top posts whatever range says (time-windowed tops are a known gap here; reddit_search's range DOES work for keyword queries). Costs 4 credits per page; a cursor page is a NEW call priced the same way. Reddit splices about one promoted ad into every feed page; these are dropped by default, so set include_promoted=true only when ads ARE the data you want (ad intelligence, who targets this community) -- kept ads carry is_promoted=true. Returns post summaries (title, author, upvotes, comment_count, created_at, permalink, id) with a cursor for older posts; bodies and discussions come from reddit_get_post with the returned ids. For keyword search across all of Reddit use reddit_search; this tool takes no query.

reddit_get_user

Fetch one Reddit user's public profile: username, account age, karma, follower count, and description. username is the name without the u/ prefix, e.g. 'spez'; a full profile URL also works. Add up to 2 sections from comments|posts|subreddits at 2 credits each: comments and posts return that user's recent activity (first page), subreddits returns where they are active. Costs 4 credits base. Use this to profile loud voices found via reddit_search before quoting or engaging them. To find users by topic, use reddit_search type=users; this tool needs an exact username.

reddit_get_post

Fetch full post content for up to 100 Reddit posts by their t3_ ids in one call -- the follow-up loop after reddit_search or reddit_get_subreddit_posts. Costs 4 credits for up to 10 ids, +1 credit per further 10 ids; every post comes back with its full body text. With exactly ONE id you may set detail to 'full' for +4 credits to also get the discussion tree (comments flattened in tree order with depth, about 200 per page, with a comments_cursor to continue), or pass comment_id (a t1_ id, also +4 credits) to fetch one specific comment in its post context. Post ids come from the other Reddit tools or from reddit_resolve_url on a post URL. Not for discovering posts; search first, then batch-fetch here.

reddit_resolve_url

Identify what a Reddit URL points at before fetching it. Give any reddit.com or redd.it URL (share links, old.reddit.com, trailing params are fine); get back {type: subreddit|user|post|comment, id, handle, canonical_url}. Post URLs yield the t3_ id for reddit_get_post; comment permalinks yield the t1_ id; subreddit and user URLs yield the name for reddit_get_subreddit or reddit_get_user. Costs 2 credits and parses offline without fetching the page. Skip this tool when you already have a t3_/t1_ id, subreddit name, or username: the other Reddit tools accept those directly. Not for non-Reddit URLs; it returns INVALID_INPUT for those.

x_search

Search X by keywords. type picks the mode: recent (default) is the deep chronological sweep and keeps paginating as far as you follow the cursor; popular returns the highest-engagement tweets for the query; people finds accounts and returns a single page (no cursor). Advanced query operators pass through verbatim, e.g. "from:nasa", "min_faves:100", exact phrases in quotes -- there is no separate date parameter, so use since:/until: operators for time windows. Costs 6 credits per page; a cursor page is a NEW call priced the same way, so a deep sweep costs linearly in pages. Returns tweet summaries (text, author with follower count, views, likes, retweets, replies, created_at, url, id) with a cursor. Fetch one tweet's full detail with x_get_tweet and an account's timeline with x_get_tweets. For comment-level sentiment inside topic communities, reddit_search type=comments is usually the sharper instrument.