A
license-
qualityC
maintenanceMulti-agent memory intelligence with expertise tracking and conflict
detection
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1
MIT
similarity-search-api-sdk
Server Details
Stateless NMI + cosine fusion with entropy-driven alpha calibration
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- nexus-mcp-infra/similarity-search-api-sdk
- GitHub Stars
- 0
- Server Listing
- Similarity Search MCP Server
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Tool Definition Quality
A4.7/5.0
Tool DescriptionsA
Average 4.6/5 across 3 of 3 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a clearly distinct purpose: estimate corpus entropy, rank items by fusion, and score a single pair. Descriptions explicitly clarify boundaries and usage, leaving no ambiguity.
Naming Consistency5/5
All tools follow the exact same pattern: 'nexus_similarity_search_api_<verb>_<noun>'. The naming is fully consistent and predictable.
Tool Count5/5
Three tools are appropriate for this specialized API. Each covers a distinct operation (estimation, ranking, scoring) without unnecessary duplication. The count is minimal but sufficient.
Completeness5/5
The tool set covers the core workflow: entropy estimation for calibration, corpus ranking, and single-pair scoring for debugging. No obvious gaps for the stated purpose of NMI-cosine fusion search.
Available Tools
3 toolsnexus_similarity_search_api_estimate_corpus_entropy_profileAInspect
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 a valid api_key (same as X-API-Key) and an x402 payment.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| n_bins | No | 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. | |
| api_key | Yes | API key required for this paid operation -- same secret configured as X-API-Key on the REST endpoints (SIMILARITY_API_KEY). Payment (x402) alone is not sufficient; both gates must pass. | |
| corpus_vectors | Yes | 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. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the operation is paid and requires both api_key and x402 payment, and that it returns only an aggregate, not per-dimension. It implies no side effects or destructive actions. Could mention any rate limits or cost implications, but overall adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences with clear, front-loaded purpose. No fluff. Each sentence adds essential information. The warning about per-dimension is placed appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, full schema coverage, and an output schema (inferred), the description provides sufficient context about the paid/authenticated nature, return format, and connection to sibling tools. Minor gaps could be explicitly stating the output type, but the description already mentions 'single aggregate corpus_entropy value'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by linking n_bins to the sibling function for consistency, explaining that api_key must match X-API-Key and that payment is also required. It also clarifies the single return value beyond what schema likely provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes an aggregate entropy-calibrated alpha without running a full search, distinguishing it from the sibling rank_items_by_nmi_cosine_fusion. It specifies the exact output (corpus_entropy) and warns against expecting per-dimension granularity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly recommends using this tool before a large rank_items_by_nmi_cosine_fusion call and warns not to use it for per-dimension breakdown. It mentions prerequisites (api_key and x402 payment). It does not contrast with the third sibling, score_pair_nmi_cosine, but the primary alternative is covered.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nexus_similarity_search_api_rank_items_by_nmi_cosine_fusionAInspect
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 a valid api_key (same as X-API-Key) and an x402 payment.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| top_k | No | Number of top-ranked results to return, ordered by descending fusion score. Capped at 1000 by the core service regardless of corpus size. | |
| n_bins | No | Number of histogram bins used to discretize continuous dimensions when estimating NMI. Must be between 3 and 50. | |
| api_key | Yes | API key required for this paid operation -- same secret configured as X-API-Key on the REST endpoints (SIMILARITY_API_KEY). Payment (x402) alone is not sufficient; both gates must pass. | |
| query_vector | Yes | Dense numeric vector representing the query item. Must have the same dimensionality as all corpus_vectors entries. | |
| alpha_override | No | 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. | |
| corpus_vectors | Yes | List of dense numeric vectors forming the corpus to rank against. Each inner array must match query_vector dimensionality. Maximum 500000 entries. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description comprehensively discloses behavioral traits: result identification by index, alpha auto-calibration, size limits, and payment gates, leaving no critical gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single dense paragraph front-loads action and formula, with no redundant sentences. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given absence of annotations, the description covers usage, constraints, fusion logic, and requirements fully. Output schema handles return values, making it complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 6 parameters have schema descriptions, and the tool description adds contextual meaning (e.g., top_k cap, alpha override interpretation, dimensionality matching), exceeding the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool ranks a corpus using a calibrated fusion score of cosine and NMI, specifying the resource (stateless corpus) and distinguishing it from sibling tools for entropy estimation and pair scoring.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance on when to use (semantically-calibrated similarity, no vector database) and when not to (geometric nearest-neighbor, corpora >500k), plus requirements for api_key and x402 payment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nexus_similarity_search_api_score_pair_nmi_cosineAInspect
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 a valid api_key (same as X-API-Key) and an x402 payment.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| alpha | No | 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. | |
| n_bins | No | Histogram bins for NMI discretization. Must be between 3 and 50. | |
| api_key | Yes | API key required for this paid operation -- same secret configured as X-API-Key on the REST endpoints (SIMILARITY_API_KEY). Payment (x402) alone is not sufficient; both gates must pass. | |
| vector_a | Yes | First dense numeric vector of the pair. Must have the same dimensionality as vector_b. | |
| vector_b | Yes | Second dense numeric vector of the pair. Must have the same dimensionality as vector_a. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It discloses alpha is not auto-calibrated, and both api_key and payment are required. However, it does not mention safety (e.g., read-only nature) or error behavior. Adds useful context but could be more complete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with 4-5 sentences, each adding distinct value (purpose, usage, limitation, requirement). No redundancy, but slight room for brevity (e.g., merging some clauses). Front-loaded with core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity (fusion score, alpha, bins) and presence of output schema, description covers purpose, usage, alternatives, and requirements. Lacks explanation of output semantics, but output schema likely handles that. Overall sufficient for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond schema: explains alpha's fixed behavior, api_key requirement duality, and usage restrictions for looping. Provides valuable context not in schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool computes the NMI-cosine fusion score for exactly one vector pair, with specific use cases (explainability, debugging, unit-level validation). It distinguishes from sibling tool 'rank_items_by_nmi_cosine_fusion' by explicitly noting not to use in a loop.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use (single pair, debugging) and when-not-to-use (not in loop, use rank_items_by_nmi_cosine_fusion for batching). Also specifies requirements (valid api_key and x402 payment).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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