Enhanciar — company brain
Ask your team's code, Slack and docs from any MCP client. Every answer cited to source. Early access
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"enhanciar": {
"url": "https://enhanciar.in/enhanciar/mcp/"
}
}
}Remote-Endpunkte
https://enhanciar.in/enhanciar/mcp/streamable-httpWas es kann
Tool-Inventar
Tools (12)
🟢list_pages
List every wiki page the caller can see in the key's workspace. Returns a list of ``{category, name, title}`` records — feed each one to ``get_page`` to fetch the full markdown body. Use this first when you want a directory view; for a specific question prefer ``query`` which handles retrieval for you.
Eingabe-Schema
{
"type": "object",
"properties": {},
"title": "list_pagesArguments"
}Ausgabe-Schema
{
"type": "object",
"properties": {
"result": {
"items": {
"additionalProperties": {
"type": "string"
},
"type": "object"
},
"title": "Result",
"type": "array"
}
},
"required": [
"result"
],
"title": "list_pagesOutput"
}🟢search_wiki(query, limit)
Substring search across the wiki pages in the key's workspace. Cheap and deterministic — no embedding required. Returns up to ``limit`` (default 20, max 50) ``{category, name, title, snippet}`` hits. The ``snippet`` is a short window around the first match, safe to surface in chat as a citation preview. For semantic / natural-language questions prefer ``query``.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
},
"limit": {
"default": 20,
"title": "Limit",
"type": "integer"
}
},
"required": [
"query"
],
"title": "search_wikiArguments"
}Ausgabe-Schema
{
"type": "object",
"properties": {
"result": {
"items": {
"additionalProperties": true,
"type": "object"
},
"title": "Result",
"type": "array"
}
},
"required": [
"result"
],
"title": "search_wikiOutput"
}🟢get_page(category, name)
Fetch a single wiki page from the key's workspace. Args: category: One of ``entities | concepts | people | decisions | sources | flows | infrastructure | tickets``. name: Page slug (without ``.md`` extension), as returned by ``list_pages`` / ``search_wiki``. Returns ``{category, name, content}`` — ``content`` is the full markdown body including YAML frontmatter. Returns ``{error: "..."}`` if the page doesn't exist.
Eingabe-Schema
{
"type": "object",
"properties": {
"category": {
"title": "Category",
"type": "string"
},
"name": {
"title": "Name",
"type": "string"
}
},
"required": [
"category",
"name"
],
"title": "get_pageArguments"
}Ausgabe-Schema
{
"type": "object",
"additionalProperties": true,
"title": "get_pageDictOutput"
}🟡query(question, model)
Ask Enhanciar a natural-language question grounded in the key's workspace. This is the high-level tool — use it for any "what does X do", "why did we choose Y", "where is Z handled" question. It runs the full retrieval + synthesis pipeline and returns the answer plus citations. Args: question: Natural-language question. model: Optional model id override (``gemini-2.5-flash``, ``gpt-4o``, ``claude-sonnet-4-5``, etc.). If omitted the saved default is used — the workspace's in a team workspace, the caller's own in a personal one. Returns ``{answer, sources, model}`` — ``sources`` is a list of ``{category, name, url}`` citations the LLM grounded its answer in. Surface them to the human so they can verify. When the workspace has nothing ingested at all you get ``{answer, empty_brain: true}`` and no sources: that is a statement about the workspace, not a failed search, so rephrasing the question will not change it.
Eingabe-Schema
{
"type": "object",
"properties": {
"question": {
"title": "Question",
"type": "string"
},
"model": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Model"
}
},
"required": [
"question"
],
"title": "queryArguments"
}Ausgabe-Schema
{
"type": "object",
"additionalProperties": true,
"title": "queryDictOutput"
}🟡impact(target, depth)
Compute the blast radius of changing ``target`` in the key's workspace. Use this before editing code to see what a change ripples into: direct callers/callees, every affected file, the affected tests, the transitive dependency set, and example dependency paths with per-path facts (hops, dependents at the endpoint, whether it lands in a test). There is deliberately no risk score. There was one — thresholds on the transitive count — and it was a verdict the caller could not check or argue with. The counts it was computed from are all here; judge from those. Args: target: A graph node id, wiki page name, file path (``micrograd/engine.py``), or a function fqn (``engine.py::func``). depth: How many hops to traverse the call graph (default 2). Returns the impact dict — ``{target, label, found, direct_callers, direct_callees, affected_files, affected_tests, transitive_nodes, transitive_count, paths, path_facts}``.
Eingabe-Schema
{
"type": "object",
"properties": {
"target": {
"title": "Target",
"type": "string"
},
"depth": {
"default": 2,
"title": "Depth",
"type": "integer"
}
},
"required": [
"target"
],
"title": "impactArguments"
}Ausgabe-Schema
{
"type": "object",
"additionalProperties": true,
"title": "impactDictOutput"
}🟢get_graph
Return the community/knowledge graph manifest for visualisation. Useful when the calling agent wants the high-level structure of the key's workspace (clusters, hub nodes, cross-references) rather than the contents of any one page. Returns ``{nodes, edges, communities, stats}`` — exact shape mirrors the ``/api/wiki/graph`` REST endpoint.
Eingabe-Schema
{
"type": "object",
"properties": {},
"title": "get_graphArguments"
}Ausgabe-Schema
{
"type": "object",
"additionalProperties": true,
"title": "get_graphDictOutput"
}🟢list_repos
List the repos ingested into the key's workspace. Returns ``{full_name, branch, last_ingested_at}`` records. Use this when the agent needs to know what context is available before asking a question. Reads what ingest actually recorded for this namespace: ``repos.json`` (the repo→clone map every other read path resolves through) plus the per-repo ``_state`` files that carry the branch and the last run's timestamp. It used to read a Firestore subcollection ``users/{uid}/repos``, which was wrong twice over. It was keyed by the person rather than the workspace — the bug this module was fixed for — and nothing in the codebase has ever written to that path, so the tool returned an empty list to everyone, forever. Hence ``branch`` rather than the old ``default_branch``: the state file records the branch that was actually ingested, and no caller can be depending on a key that never had a row under it.
Eingabe-Schema
{
"type": "object",
"properties": {},
"title": "list_reposArguments"
}Ausgabe-Schema
{
"type": "object",
"properties": {
"result": {
"items": {
"additionalProperties": true,
"type": "object"
},
"title": "Result",
"type": "array"
}
},
"required": [
"result"
],
"title": "list_reposOutput"
}🟢list_skills
List the compiled skills (recurring procedures) in the key's workspace. Skills are agent-executable SKILL.md pages mined from that workspace's wiki by the skills compiler. Returns ``{name, title, description, confidence, last_compiled, sources}`` records — feed a ``name`` to ``get_skill`` for the full markdown. Empty list = nothing compiled yet (the user can run a compile from Enhanciar's UI or API).
Eingabe-Schema
{
"type": "object",
"properties": {},
"title": "list_skillsArguments"
}Ausgabe-Schema
{
"type": "object",
"properties": {
"result": {
"items": {
"additionalProperties": true,
"type": "object"
},
"title": "Result",
"type": "array"
}
},
"required": [
"result"
],
"title": "list_skillsOutput"
}🟢get_skill(name)
Fetch one compiled skill by name (slug from ``list_skills``). Returns ``{name, markdown, meta}`` — ``markdown`` is the full agent-executable SKILL.md content (frontmatter + steps), ``meta`` is the parsed frontmatter. Includes an ``error`` key when the skill doesn't exist (with the available names).
Eingabe-Schema
{
"type": "object",
"properties": {
"name": {
"title": "Name",
"type": "string"
}
},
"required": [
"name"
],
"title": "get_skillArguments"
}Ausgabe-Schema
{
"type": "object",
"additionalProperties": true,
"title": "get_skillDictOutput"
}🟢get_process_map
Return the "living map of how the company works" for the key's workspace. A graph of process nodes (compiled skills), the external systems they touch (GitHub, Slack, Jira, …), the wiki docs they were derived from, and team groups — with derived_from / uses / references edges. Use this when the agent wants the high-level operational structure rather than one skill's steps.
Eingabe-Schema
{
"type": "object",
"properties": {},
"title": "get_process_mapArguments"
}Ausgabe-Schema
{
"type": "object",
"additionalProperties": true,
"title": "get_process_mapDictOutput"
}🟢list_proposed_actions(status)
List the key's workspace's proposed actions (the approval queue). Read-only. Optionally filter by ``status`` (proposed / approved / executing / done / failed / dismissed). Returns ``{id, action_type, status, source, title, source_doc_id, external_id}`` per proposal. NOTE: MCP can read and PROPOSE actions but deliberately cannot approve or execute them — a human approves every action in the app UI.
Eingabe-Schema
{
"type": "object",
"properties": {
"status": {
"default": "",
"title": "Status",
"type": "string"
}
},
"title": "list_proposed_actionsArguments"
}Ausgabe-Schema
{
"type": "object",
"properties": {
"result": {
"items": {
"additionalProperties": true,
"type": "object"
},
"title": "Result",
"type": "array"
}
},
"required": [
"result"
],
"title": "list_proposed_actionsOutput"
}⚪propose_action(action_type, title, description, evidence)
Propose a new action for human approval (does NOT execute it). ``action_type`` is a registered executor id (e.g. ``jira.create_issue``, ``linear.create_issue``, ``github.create_issue``). The proposal lands in the key's workspace's approval queue where a person picks the target and approves it — approve/execute are intentionally NOT exposed over MCP. Returns the created ``{id, action_type, status}`` or an ``error``.
Eingabe-Schema
{
"type": "object",
"properties": {
"action_type": {
"title": "Action Type",
"type": "string"
},
"title": {
"title": "Title",
"type": "string"
},
"description": {
"default": "",
"title": "Description",
"type": "string"
},
"evidence": {
"default": "",
"title": "Evidence",
"type": "string"
}
},
"required": [
"action_type",
"title"
],
"title": "propose_actionArguments"
}Ausgabe-Schema
{
"type": "object",
"additionalProperties": true,
"title": "propose_actionDictOutput"
}Empfohlene Prompts
get_pagesearch_wikiget_pagesearch_wikilist_pagesCommunity
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