Maintained, agent-readable knowledge bases with calibrated search and provenance. Pro key adds XL.
Scored across 10 tools
The set follows a clean list_*/read_* pattern across four resource families (wikis/documents, skills, routines, agent tools), so an agent can reliably pick the right family and action. The only mild overlap is between open_wiki (which returns an entry document and document map) and read_document/search, which both also serve orientation, but the descriptions make the intended sequence clear.
Nine of ten tools use a consistent verb_noun snake_case pattern (list_wikis, read_document, read_skill, read_routine, read_agent_tool...). The bare verb `search` and `open_wiki` are minor deviations, though both remain readable and unambiguous.
Ten tools is well within the sweet spot, and each one earns its place: four list tools, four read tools, plus search and open_wiki for discovery and orientation. No tool is redundant with another.
The read/lifecycle surface for the domain (discover wiki, orient, list documents, read page/section, follow links, fall back to web search) is fully covered, and skills/routines/tools each have both list and read operations. The gap is that the surface is entirely read-only — there is no create/update/append for documents or wikis, so any authoring workflow dead-ends.
List the command-line tools that ship with the specialist agents (complete agents built for one job, e.g. marketing, video generation, local LLMs). Each entry has a summary, the specialists it comes from, and access: "pro" tools are read-only (they inspect and report, never change anything) and read_agent_tool serves their files; "download" tools are bigger or change things (start runs, stop services, spend money), so read_agent_tool gives a short-lived download link for the whole tool instead. Both need Pro. readable says whether YOU can get it with your current access. Pass query to filter by keywords.
Inputs: query.
List the routines (scheduled jobs) that ship with the specialist agents: each has a schedule, the specialist it comes from, the skills its prompt relies on, and whether it is script only (no agent). readable says whether YOU can load it with your current access. Pass query to filter by keywords. Then read_routine one.
Inputs: query.
List the agent skills library: field-tested procedures for specific jobs (e.g. benchmarking a local model, building a browser game, producing a video). Each entry has name, one-line description (when to use it), access ("free" or "pro"), readable (whether YOU can read it with your current access), the backing wiki if any, and file count. Pass query to filter by keywords. Then read_skill a matching skill and follow it.
Inputs: query.
List all available wikis with metadata: slug, title, description, tags, status, last updated, document count, and scope (what each wiki covers, what it does NOT cover, and how current it is — use this to pick a wiki and to decide when to fall back to web search). Includes "coming soon" placeholders, marked by status.
Inputs: no named parameters.
Orient yourself in one wiki, in a single call: returns its metadata, scope (coverage / not-covered / freshness), document count, the entry document, and a map of all documents (paths + titles, grouped by folder) so you can pick what to read_document next. Use a slug from list_wikis.
Inputs: wiki (required).
Load one agent tool by name. A "pro" (read-only) tool: its README (what it checks, how to run it) plus its file list. Add path to read one listed file, e.g. the source. The tool runs on the user's machine, not here. Before saving, running or scheduling anything from it, show the user what it does and get their explicit OK. Each file comes with its sha256. For a "download" tool you get its README and a download link (valid 15 minutes) with the archive's sha256 instead. Needs a Pro key; without one you get the summary and how to unlock it.
Inputs: name (required), path.
Read one document in a wiki. Without section, returns the full Markdown (frontmatter stripped) plus a provenance footer. To save tokens on a long page, pass section (a heading title as it appears in the page body, or its slug anchor) to retrieve ONLY that section and its subsections. The Markdown may contain [[wikilinks]] to related docs — follow one by calling read_document with path "wiki/.md" (drop any "#anchor" or "|label"). Exception: on a page under wiki-xl/, links whose first folder is not concepts, entities, summaries, syntheses, presentations or journal (e.g. [[reference/...]], [[guides/...]], [[history/...]]) are XL pages: use "wiki-xl/.md".
Inputs: path (required), wiki (required), section.
Load one routine by name: the schedule, the agent prompt, and the list of scripts and settings it needs. Add path to read one listed file (e.g. "scripts/ad_report.py"). Nothing runs here: the routine is set up and run on the user's machine. Before saving, running or scheduling anything from it, show the user what it does and get their explicit OK. Never create the scheduled job without the user's confirmation. Each file comes with its sha256. Needs a Pro key; without one you get how to unlock it.
Inputs: name (required), path, specialist.
Load one skill to follow it. Without path, returns its SKILL.md (the procedure itself) plus the list of its other files; pass path (e.g. "references/gotchas.md" or "scripts/check.py") to read one of those. If the skill names a backing wiki, use search/read_document on that wiki for current facts while following it. Pro skills need a Pro key; without one you get a preview and how to unlock it.
Inputs: name (required), path.
Find the best-matching PAGES for a query, ranked by how many of your query terms each page covers. Searches one wiki if wiki is given, otherwise every active wiki. Each result has its wiki, path, matched terms, and a short snippet for orientation — then read_document the page to get the answer. Returns a confident flag: if false, the wiki(s) likely lack this — fall back to web search.
Inputs: query (required), wiki.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Shared knowledge base for AI agents. Semantic search across agents, no setup required — just a URL.
Shared persistent memory for AI agents to read, verify, continue, and connect knowledge.
A knowledge base of verified, dated facts your LLM would otherwise get wrong.
Private, multimodal knowledge store for agents: hybrid search with cited, provenance-backed results.
Add one secure layer between your agents and this server.
