ECCA
Compare engine claims, inspect dissent and check study evidence for student questions.
使うべきか
品質と安全性
ツール定義とプロトコルへの準拠に関する自動分析に基づいています。
コンテキストコスト
これは、サーバーのツールがモデルのコンテキストに読み込まれるたびに消費されるおおよそのトークン数です。数が多いほど、ほかのタスクに使える注意が減ります。
インストール
ワンクリックインストール
これを `claude_desktop_config.json` ファイルに追加してください:
{
"mcpServers": {
"ecca": {
"url": "https://eccaai.com/api/mcp"
}
}
}リモートエンドポイント
https://eccaai.com/api/mcpstreamable-httpできること
ツール一覧
ツール（11）
⚪ecca_synthesize(query, model, depth, antagonist)
Cross-examines inquiry across configured frontier engines to synthesize the verified single best answer with measured lexical consensus scoring.
入力スキーマ
{
"type": "object",
"properties": {
"query": {
"type": "string",
"maxLength": 64000,
"description": "The core inquiry, hypothesis, or decision challenge to synthesize."
},
"model": {
"type": "string",
"enum": [
"axiom-vector",
"axiom-core",
"axiom-reasoner",
"axiom-omni",
"axiom-sovereign"
],
"default": "axiom-reasoner",
"description": "Label echoed in the result. Every value dispatches through the same registered engines; depth decides fast or multi-engine."
},
"depth": {
"type": "string",
"enum": [
"intuitive",
"executive",
"pioneer",
"fast",
"deep"
],
"default": "executive",
"description": "intuitive and fast route to one engine; executive, pioneer and deep route a multi-engine dispatch (free callers: two engines first, expanding on measured divergence, one deep solve per day). Any other value is refused."
},
"antagonist": {
"type": "boolean",
"default": false,
"description": "When true, forces one engine to act as adversarial red-team antagonist."
}
},
"required": [
"query"
],
"additionalProperties": false
}🟢ecca_pre_mortem(premise, horizonMonths)
Executes an adversarial pre-mortem identifying failure modes and early warning indicators using empirical argument verification.
入力スキーマ
{
"type": "object",
"properties": {
"premise": {
"type": "string",
"maxLength": 64000,
"description": "The strategic plan, architecture, or business premise to stress-test."
},
"horizonMonths": {
"type": "integer",
"default": 18,
"description": "Time horizon in months to simulate forward failure modes."
}
},
"required": [
"premise"
],
"additionalProperties": false
}🟢ecca_decision_tournament(decisionContext, options)
Evaluates multi-option decision matrix using minimax regret and Pareto ranking over candidate pathways.
入力スキーマ
{
"type": "object",
"properties": {
"decisionContext": {
"type": "string",
"maxLength": 32000,
"description": "Context of the decision to evaluate."
},
"options": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of discrete options or strategic pathways."
}
},
"required": [
"decisionContext",
"options"
],
"additionalProperties": false
}🟢ecca_bts_divergence(assertions)
Measures lexical and semantic divergence across candidate assertions to penalize sycophancy.
入力スキーマ
{
"type": "object",
"properties": {
"assertions": {
"type": "array",
"items": {
"type": "string"
},
"description": "Array of candidate assertions or claims to measure divergence for."
}
},
"required": [
"assertions"
],
"additionalProperties": false
}🟢ecca_refine(text)
Use one keyed tier-quota slot to sharpen rough student questions without adding facts; return detected niches and refinement chips. A closed engine ceiling falls back locally.
入力スキーマ
{
"type": "object",
"properties": {
"text": {
"type": "string",
"minLength": 1,
"maxLength": 2000
}
},
"required": [
"text"
],
"additionalProperties": false
}🟢ecca_research(question, niche)
Use one keyed tier-quota slot and dispatch-ceiling reservation to fetch and grade studies from scholarly indexes; return fetched evidence and explicit gaps.
入力スキーマ
{
"type": "object",
"properties": {
"question": {
"type": "string",
"minLength": 1,
"maxLength": 2000
},
"niche": {
"type": "string",
"enum": [
"psychology",
"learning",
"nutrition",
"money"
]
}
},
"required": [
"question"
],
"additionalProperties": false
}🟢ecca_verify(question, candidate_answer)
Independently answer a question and compare the candidate against deterministic panel agreement. Show named dissent; spends one solve.
入力スキーマ
{
"type": "object",
"properties": {
"question": {
"type": "string",
"minLength": 1,
"maxLength": 8000
},
"candidate_answer": {
"type": "string",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"question",
"candidate_answer"
],
"additionalProperties": false
}🟢ecca_psychology(question)
Psychology research: refine the question, fetch studies, and show named claims before the verdict. Applies evidence and claims checks; spends one solve.
入力スキーマ
{
"type": "object",
"properties": {
"question": {
"type": "string",
"minLength": 1,
"maxLength": 2000
}
},
"required": [
"question"
],
"additionalProperties": false
}🟢ecca_learning(question)
Learning and study skills: refine the question, fetch studies, and show named claims before the verdict. Applies evidence and claims checks; spends one solve.
入力スキーマ
{
"type": "object",
"properties": {
"question": {
"type": "string",
"minLength": 1,
"maxLength": 2000
}
},
"required": [
"question"
],
"additionalProperties": false
}🟢ecca_nutrition(question)
Nutrition and supplements: refine the question, fetch studies, and show named claims before the verdict. Applies evidence and claims checks; spends one solve.
入力スキーマ
{
"type": "object",
"properties": {
"question": {
"type": "string",
"minLength": 1,
"maxLength": 2000
}
},
"required": [
"question"
],
"additionalProperties": false
}🟢ecca_money(question)
Money and business: refine the question, fetch studies, and show named claims before the verdict. Applies evidence and claims checks; spends one solve.
入力スキーマ
{
"type": "object",
"properties": {
"question": {
"type": "string",
"minLength": 1,
"maxLength": 2000
}
},
"required": [
"question"
],
"additionalProperties": false
}コミュニティ
エビデンス