embedding-search
Server Details
Cloudflare Workers MCP server: embedding-search
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.5/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: computing similarity between two texts, generating embeddings, and ranking documents. No overlap in functionality.
Tools use snake_case with verb phrases, but 'semantic_search' is less consistent than a verb-led pattern like 'search_semantic'. Still clear and predictable.
Three tools cover core embedding operations: generation, similarity computation, and search. The scope is focused, and each tool earns its place.
The tool set covers the primary workflow of generating embeddings and performing searches. Minor gaps like embedding storage or deletion exist but don't hinder the main use case.
Available Tools
3 toolscompute_similarityBInspect
Compute cosine similarity between two texts. Returns score in [-1,1]. Useful for dedup and relatedness.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| text_a | Yes | ||
| text_b | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description should disclose all behavioral traits. It only states it computes similarity and returns a score, but omits details like statelessness, rate limits, or edge cases.
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?
Two concise sentences with minimal waste. Includes key return value range. Could be slightly more structured but effective.
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?
Three parameters (one optional), no output schema, no annotations. The description lacks detail on parameters, output format beyond score range, and edge cases, making it incomplete.
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?
Schema coverage is 0% (no parameter descriptions in schema), and the description does not explain parameters beyond 'two texts'. The 'model' parameter is entirely unaddressed.
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 clearly states it computes cosine similarity between two texts and specifies the return range [-1,1]. This differentiates it from sibling tools like 'generate_embeddings' and 'semantic_search'.
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?
Mentions use cases ('dedup and relatedness'), providing context for when to use. Does not explicitly exclude alternatives or mention when not to use, but the use case guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_embeddingsAInspect
Generate vector embeddings for one or more texts using Cloudflare Workers AI (bge-base-en-v1.5, 768-dim).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text or array of texts to embed | |
| model | No | CF AI model ID override |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the underlying model and dimensions but, with no annotations present, fails to mention behavior such as rate limits, authentication requirements, or potential side effects. It provides minimal behavioral context beyond the core operation.
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 a single sentence that efficiently conveys the tool's purpose, model, and dimensionality with no superfluous words.
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?
The description lacks details about the return format (e.g., structure of embedding vectors) and does not explain the optional 'model' parameter. Given no output schema and moderate complexity, more completeness would be beneficial.
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?
Schema coverage is 100% with descriptions for both parameters. The description adds no additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.
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 clearly states the verb 'Generate' and resource 'vector embeddings', specifies the model (bge-base-en-v1.5) and dimensionality (768-dim), and distinguishes it from sibling tools that process rather than generate embeddings.
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?
No guidance is provided on when to use this tool vs its siblings (compute_similarity, semantic_search). The description does not mention context or prerequisites, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
semantic_searchAInspect
Rank a list of documents against a query using cosine similarity of bge embeddings. Returns top-k matches with scores.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | CF AI model ID override | |
| query | Yes | Search query | |
| top_k | No | Number of results (default 5) | |
| documents | Yes | Candidate documents to rank |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the use of cosine similarity and bge embeddings, and clarifies the return of top-k results with scores. However, it does not explicitly state whether the operation is read-only or if there are authentication or rate limit considerations.
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 extremely concise, consisting of two clear sentences with no unnecessary words. It directly states the action, method, and output.
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?
Given the schema covers all parameters and there is no output schema, the description adequately explains the tool's core function and output. It could mention the output format more explicitly, but overall it is sufficient for an agent to understand usage.
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 has 100% description coverage for all parameters. The description adds minimal extra meaning beyond the schema, such as mentioning 'cosine similarity' and 'bge embeddings', but these are more about the algorithm than parameter semantics. The baseline for high coverage is 3.
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 clearly specifies the verb 'rank', the resource 'list of documents against a query', the method 'cosine similarity of bge embeddings', and the output 'top-k matches with scores'. It distinguishes itself from siblings like 'compute_similarity' and 'generate_embeddings' by focusing on ranking documents against a query.
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?
The description states what the tool does but does not explicitly guide when to use it versus alternatives like 'compute_similarity' or 'generate_embeddings'. The context is implied but lacks explicit when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!