Search Vascue's public healthcare-operations, insurance-claims and clinic-booking documentation. Public content only; never send patient data, credentials or booking requests.
Hybrid keyword + semantic search over the public pages of vascue.io: healthcare-operations guides, the AI front desk for clinics, provider-side insurance-claims automation, Cliniko and Nookal integration, security and compliance pages, case studies, pricing and blog posts.
Use it to answer questions about what Vascue offers, how its products work and what it has published. One topic per call; cite the returned page URL for every excerpt you use.
Returns a JSON text payload: result.chunks[] ordered by relevance, each with text (a Markdown excerpt), score (0-1, higher is better) and item.metadata.url (the canonical https://www.vascue.io/... page). Off-topic queries still return the nearest chunks, so treat scores below about 0.5 as weak evidence and say so when the site does not cover a topic.
Read-only and idempotent; no authentication; 60 requests per minute per client. Public content only: never send patient information, claim documents, clinic credentials or booking requests. It cannot book appointments or look up clinic data.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What to look for, as a natural-language question or keywords, e.g. "how does claims pre-authorisation work" or "Cliniko integration". 3-15 words works best; one topic per call. | |
| ai_search_options | No | Optional retrieval tuning. Omit for the defaults: hybrid search, 8 results, score threshold 0.35. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses rate limits, lack of authentication, read-only idempotency in prose, off-topic behavior, and the weak-evidence threshold around score 0.5. It also explicitly warns against sending patient/claim data, adding meaningful operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and efficient: every sentence contributes a distinct operational or behavioral fact. Key constraints are front-loaded, and the structure moves from scope to usage to output format to safety policy, which is easy for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description fully explains the return payload shape and relevance-score interpretation. It also covers limits, permissions, off-topic behavior, and prohibited use cases, making it complete for safe and correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes all parameters with exhaustive detail, including defaults and tuning behavior, so description-level parameter guidance is not needed. The description still adds useful usage context like 'one topic per call' and citation expectations, but it does not need to restate schema semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb/search resource and a bounded scope: hybrid keyword and semantic search over public vascue.io pages. It enumerates the covered content types and explicitly says what it cannot do, which distinguishes it even without siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It directly tells the agent when to use the tool: to answer questions about what Vascue offers, how its products work, and what it has published. It also gives clear exclusions: it cannot book appointments, look up clinic data, or handle sensitive material, so misuse is prevented.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
search15 fields changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Input schema / properties / ai_search_options / descriptionPrevious value: -"Options for customizing the AI search behavior."New value: +"Optional retrieval tuning. Omit for the defaults: hybrid search, 8 results, score threshold 0.35."Input schema / properties / ai_search_options / properties / cacheRemoved value: -{
- "properties": {
- "cache_threshold": {
- "type": "string"
- },
- "enabled": {
- "type": "boolean"
- }
- },
- "type": "object"
-}Input schema / properties / ai_search_options / properties / query_rewriteRemoved value: -{
- "properties": {
- "enabled": {
- "const": false,
- "type": "boolean"
- },
- "rewrite_prompt": {
- "type": "string"
- }
- },
- "type": "object"
-}Input schema / properties / ai_search_options / properties / rerankingRemoved value: -{
- "properties": {
- "enabled": {
- "const": false,
- "type": "boolean"
- },
- "match_threshold": {
- "maximum": 1,
- "minimum": 0,
- "type": "number"
- }
- },
- "type": "object"
-}Input schema / properties / ai_search_options / properties / retrieval / properties / context_expansion / descriptionAdded value: +"Neighbouring chunks from the same page to include around each hit (default 0)."Input schema / properties / ai_search_options / properties / retrieval / properties / filtersRemoved value: -{
- "additionalProperties": {
- "anyOf": [
- {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- },
- {
- "type": "boolean"
- },
- {
- "type": "null"
- }
- ]
- },
- {
- "properties": {
- "$eq": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- },
- {
- "type": "boolean"
- },
- {
- "type": "null"
- }
- ]
- },
- "$gt": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- }
- ]
- },
- "$gte": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- }
- ]
- },
- "$in": {
- "items": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- },
- {
- "type": "boolean"
- },
- {
- "type": "null"
- }
- ]
- },
- "type": "array"
- },
- "$lt": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- }
- ]
- },
- "$lte": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- }
- ]
- },
- "$ne": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- },
- {
- "type": "boolean"
- },
- {
- "type": "null"
- }
- ]
- },
- "$nin": {
- "items": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "number"
- },
- {
- "type": "boolean"
- },
- {
- "type": "null"
- }
- ]
- },
- "type": "array"
- }
- },
- "type": "object"
- }
- ]
- },
- "propertyNames": {
- "type": "string"
- },
- "type": "object"
-}Input schema / properties / ai_search_options / properties / retrieval / properties / fusion_methodRemoved value: -{
- "enum": [
- "max",
- "rrf"
- ],
- "type": "string"
-}Input schema / properties / ai_search_options / properties / retrieval / properties / match_threshold / descriptionAdded value: +"Minimum relevance score for a chunk to be included (default 0.35); raise for precision, lower for recall."Input schema / properties / ai_search_options / properties / retrieval / properties / max_num_results / descriptionAdded value: +"Maximum chunks to return (default 8)."Input schema / properties / ai_search_options / properties / retrieval / properties / metadata_onlyRemoved value: -{
- "type": "boolean"
-}Input schema / properties / ai_search_options / properties / retrieval / properties / retrieval_type / descriptionAdded value: +"hybrid (default) fuses keyword and vector ranking; keyword for exact phrases or product names; vector for paraphrased questions."Input schema / properties / ai_search_options / properties / retrieval / properties / retrieval_type / enumPrevious value: -[
- "vector",
- "keyword",
- "hybrid"
-]New value: +[
+ "hybrid",
+ "vector",
+ "keyword"
+]Input schema / properties / ai_search_options / properties / retrieval / properties / return_on_failureRemoved value: -{
- "type": "boolean"
-}Input schema / properties / query / descriptionPrevious value: -"The input query."New value: +"What to look for, as a natural-language question or keywords, e.g. \"how does claims pre-authorisation work\" or \"Cliniko integration\". 3-15 words works best; one topic per call."searchClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Add one secure layer between your agents and this server.
With only one tool, there is no possibility of confusion between tools. The sole 'search' tool has a clear, singular purpose that an agent can unambiguously select.
The single tool name 'search' is a simple, predictable verb-form name. While there is no broader pattern to evaluate, it is internally consistent and clear on its own.
A single search tool covers the core reading use case but feels minimal for a server that also receives off-topic queries and relies on score thresholds. Borderline-thin, yet appropriate if the scope is strictly a public knowledge search.
The tool fully addresses the stated purpose: hybrid keyword + semantic search over all indicated public pages. No other operations are expected for a read-only public knowledge search, so there are no obvious gaps.