Server definition
- Hash
- sha256:88887d29267f77267cc06ac31bc25239b652b8025316830fd6fbe6ca8f50d6b4
- What it is
- What a remote MCP server returned when asked what it offers: 3 tools
The blob, as servednamed by its sha256
{
"instructions": null,
"tools": [
{
"description": "Computes the aggregate entropy-calibrated alpha for a corpus without running a full search -- useful to inspect before committing to a large rank_items_by_nmi_cosine_fusion call. Returns a single aggregate corpus_entropy value, NOT a per-dimension breakdown -- the real logic only exposes the mean marginal entropy across dimensions, not H(X_d) per individual dimension. Do NOT use expecting per-dimension granularity. Requires an x402 payment.",
"inputSchema": {
"properties": {
"corpus_vectors": {
"description": "List of dense numeric vectors for which to compute the aggregate entropy and calibrated alpha. Each inner array must be the same length. Maximum 500000 entries.",
"items": {
"items": {
"type": "number"
},
"type": "array"
},
"maxItems": 500000,
"minItems": 1,
"title": "Corpus Vectors",
"type": "array"
},
"n_bins": {
"default": 16,
"description": "Number of histogram bins for entropy discretization. Must be between 3 and 50; should match the n_bins used in rank_items_by_nmi_cosine_fusion for the profile to be consistent.",
"maximum": 50,
"minimum": 3,
"title": "N Bins",
"type": "number"
}
},
"required": [
"corpus_vectors"
],
"title": "estimate_corpus_entropy_profileArguments",
"type": "object"
},
"name": "nexus_similarity_search_api_estimate_corpus_entropy_profile",
"outputSchema": null
},
{
"description": "Ranks a corpus of items against a query vector using a calibrated fusion score (alpha * cosine + (1-alpha) * NMI_normalizado), where alpha is auto-derived from the corpus's marginal entropy unless overridden. Results are identified by their 0-indexed position in corpus_vectors (this tool does not accept explicit item IDs). Use this when you need semantically-calibrated similarity over a stateless corpus of up to 500k items without a vector database. Do NOT use for purely geometric nearest-neighbor search where NMI overhead is unnecessary, nor for corpora larger than 500k items per call. Requires an x402 payment.",
"inputSchema": {
"properties": {
"alpha_override": {
"default": null,
"description": "Fixed alpha weight for cosine component in [0.0, 1.0]. If omitted, alpha is auto-calibrated from corpus entropy. Set to 1.0 to use pure cosine; 0.0 for pure NMI.",
"maximum": 1,
"minimum": 0,
"title": "Alpha Override",
"type": "number"
},
"corpus_vectors": {
"description": "List of dense numeric vectors forming the corpus to rank against. Each inner array must match query_vector dimensionality. Maximum 500000 entries.",
"items": {
"items": {
"type": "number"
},
"type": "array"
},
"maxItems": 500000,
"minItems": 1,
"title": "Corpus Vectors",
"type": "array"
},
"n_bins": {
"default": 16,
"description": "Number of histogram bins used to discretize continuous dimensions when estimating NMI. Must be between 3 and 50.",
"maximum": 50,
"minimum": 3,
"title": "N Bins",
"type": "number"
},
"query_vector": {
"description": "Dense numeric vector representing the query item. Must have the same dimensionality as all corpus_vectors entries.",
"items": {
"type": "number"
},
"maxItems": 4096,
"minItems": 2,
"title": "Query Vector",
"type": "array"
},
"top_k": {
"default": 10,
"description": "Number of top-ranked results to return, ordered by descending fusion score. Capped at 1000 by the core service regardless of corpus size.",
"maximum": 1000,
"minimum": 1,
"title": "Top K",
"type": "number"
}
},
"required": [
"query_vector",
"corpus_vectors"
],
"title": "rank_items_by_nmi_cosine_fusionArguments",
"type": "object"
},
"name": "nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion",
"outputSchema": null
},
{
"description": "Computes the NMI-cosine fusion score for exactly one (query, target) vector pair at a fixed alpha. Use for explainability, debugging, or unit-level validation of fusion scores before running full corpus ranking. Unlike corpus-level ranking, alpha is NOT auto-calibrated for a single pair -- the real logic requires a fixed alpha (default 0.5); pass alpha explicitly for a specific blend. Do NOT use in a loop to score many pairs; batch them into rank_items_by_nmi_cosine_fusion instead. Requires an x402 payment.",
"inputSchema": {
"properties": {
"alpha": {
"default": 0.5,
"description": "Fixed alpha weight for the cosine component in [0.0, 1.0], applied as-is -- not auto-calibrated. Default 0.5 matches the core service default.",
"maximum": 1,
"minimum": 0,
"title": "Alpha",
"type": "number"
},
"n_bins": {
"default": 16,
"description": "Histogram bins for NMI discretization. Must be between 3 and 50.",
"maximum": 50,
"minimum": 3,
"title": "N Bins",
"type": "number"
},
"vector_a": {
"description": "First dense numeric vector of the pair. Must have the same dimensionality as vector_b.",
"items": {
"type": "number"
},
"maxItems": 4096,
"minItems": 2,
"title": "Vector A",
"type": "array"
},
"vector_b": {
"description": "Second dense numeric vector of the pair. Must have the same dimensionality as vector_a.",
"items": {
"type": "number"
},
"maxItems": 4096,
"minItems": 2,
"title": "Vector B",
"type": "array"
}
},
"required": [
"vector_a",
"vector_b"
],
"title": "score_pair_nmi_cosineArguments",
"type": "object"
},
"name": "nexus_similarity_search_api_score_pair_nmi_cosine",
"outputSchema": null
}
]
}Verify it yourself
curl -s https://api.teppi.xyz/v1/evidence/sha256:88887d29267f77267cc06ac31bc25239b652b8025316830fd6fbe6ca8f50d6b4 | sha256sum