mockmcp
Server Details
Hosted MCP endpoint with realistic fake data for prototyping agents. 12 tools, no setup.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- K-Cupples/mockmcp
- GitHub Stars
- 0
- Server Listing
- MockMCP
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 3.9/5 across 12 of 12 tools scored. Lowest: 2.7/5.
Each tool targets a distinct resource-action pair: create/get/list for users, orders, products, events; plus search and email. No two tools have overlapping purposes.
All tools follow a consistent verb_noun pattern (create_, get_, list_, search_, send_) using lowercase snake_case throughout.
12 tools cover multiple mock domains (users, orders, products, events, knowledge base, email) without being excessive or sparse.
Missing update and delete operations for all resources, and no get_event to complement list_events. Significant gaps for full CRUD lifecycle.
Available Tools
12 toolscreate_eventAInspect
Record a mock analytics event. Not persisted — echoes your input with a generated id and timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | ||
| event_type | Yes | ||
| properties | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors: mock, not persisted, echoes input with generated id and timestamp. No annotations exist, so description carries full burden and handles it well for a simple tool.
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 short sentences, front-loaded with purpose. No redundant or unnecessary text.
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?
Covers core behavior but leaves parameter semantics undocumented and return shape vague (no output schema). Adequate for a simple mock tool, but not fully complete.
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 description coverage is 0% and description adds no explanation for user_id, event_type, or properties. Agent must infer meaning from names alone, which may be ambiguous.
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?
Clearly states 'Record a mock analytics event' with specific verb and resource. Distinguishes from siblings like create_order/create_user by domain (event vs order/user).
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?
Implies usage for testing/development via 'mock' and 'not persisted'. Does not explicitly exclude alternatives or state when-not-to-use, but context is clear against other create and list tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_orderAInspect
Create a mock order. Not persisted — returns a freshly generated order echoing your items.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | Line items. | |
| user_id | Yes | User ID placing the order. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It transparently discloses that the order is mock and not persisted, which is critical behavioral context. However, it could mention error handling or return structure.
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 sentences concisely convey the primary purpose and key behavioral trait, with no wasted 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?
For a simple mock tool with full schema coverage, the description covers the core purpose and mock behavior. Missing details about return structure are minor given the tool's simplicity.
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%, so baseline is 3. The description adds minimal parameter meaning beyond echoing items; it doesn't elaborate on user_id or items formatting.
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 creates a 'mock order' that is not persisted, using a specific verb and resource. This distinguishes it from sibling tools like create_event and create_user.
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 implies usage for testing or demos by stating 'mock' and 'not persisted', but it does not explicitly provide when-to-use or when-not-to-use guidance, nor does it mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_userAInspect
Create a mock user. Data is not persisted — returns a fresh fake user echoing your inputs.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Full name for the new user. | |
| role | No | User role. | member |
| Yes | Email for the new user. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description transparently discloses that data is not persisted and returns a fake user echoing inputs, fully covering behavioral traits. No annotations provided, but description compensates completely.
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, front-loaded with action and key behavioral trait, no unnecessary 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?
For a simple mock creation tool with 3 parameters and no output schema, the description fully covers purpose, behavior, and output expectation, leaving no gaps.
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 each parameter. The description adds value by explaining that inputs are echoed back ('echoing your inputs'), which goes beyond schema documentation.
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 'Create a mock user' with specific verb and resource, and distinguishes from sibling tools like create_event or create_order by emphasizing the mock and non-persistent nature.
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 implies use for testing or simulation by stating 'Data is not persisted' and 'returns a fresh fake user,' but does not explicitly exclude real usage or provide alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_orderAInspect
Fetch a single mock order by ID or numeric seed.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Order ID (e.g. 'ord_abc123') or numeric seed. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description should disclose behavioral traits. It mentions 'mock order' but does not describe authentication needs, rate limits, side effects, or error behavior. Minimal transparency beyond the obvious read 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 concise sentence with no redundant information. Every word contributes to understanding.
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?
For a simple fetch tool with one parameter and no output schema, the description is nearly complete. It could mention the return format, but overall it provides sufficient context.
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 covers 100% of parameters with descriptions. The description adds an example ('ord_abc123') and clarifies that the ID can be a string or number, providing meaning beyond the schema.
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 that the tool fetches a single mock order by ID or numeric seed, using specific verbs and resource. It distinguishes from sibling tools like create_order and list_orders.
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?
Usage context is implied by the tool name and sibling tools, but the description does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_productAInspect
Fetch a single mock product by ID or numeric seed.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product ID (e.g. 'prd_abc123') or numeric seed. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description indicates 'mock product', suggesting read-only behavior, but lacks details on side effects, permissions, or return format.
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?
Single sentence, front-loaded, no superfluous words. Every word is necessary.
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?
Adequate for a simple tool with one parameter and no output schema. Minor gaps: no mention of return structure or error cases.
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% for the single parameter. Description adds 'by ID or numeric seed' which aligns with schema description; no additional semantic value beyond schema.
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?
Description clearly states verb 'Fetch', resource 'single mock product', and parameter method 'by ID or numeric seed'. It is specific and easily distinguishes from siblings like 'list_products'.
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?
Implied usage for fetching a single mock product, but no explicit guidance on when to use vs alternatives like 'list_products' or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_userAInspect
Fetch a single mock user by ID or numeric seed.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | User ID (e.g. 'usr_abc123') or numeric seed (e.g. 1). Numeric seed returns the same user as list_users would at that position. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It states the tool fetches a 'mock user', implying a read-only operation and test data. No destructive actions or special requirements are mentioned, which is adequate for a simple fetch tool.
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 short sentence with no unnecessary words, perfectly concise.
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?
For a simple fetch tool with one well-documented parameter and no output schema, the description provides enough context. It could mention return format or error handling, but these are reasonable omissions for a straightforward tool.
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 description coverage is 100%, so baseline is 3. The description adds value by explaining that 'Numeric seed returns the same user as list_users would at that position', which provides relational context beyond the schema.
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 'Fetch', the resource 'a single mock user', and the means 'by ID or numeric seed'. It distinguishes itself from sibling tools like 'list_users' (list vs. single) and 'create_user' (create vs. fetch).
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 implies usage when you need a single user by ID or seed, and hints at a relationship to 'list_users' via numeric seed. However, it does not explicitly state when to use or not use this tool compared to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_eventsCInspect
List mock analytics events, optionally filtered by user or event type.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| user_id | No | Filter by user ID. | |
| event_type | No | Filter by event type. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only says 'list' implying read-only, but does not disclose pagination behavior, rate limits, or what happens with no results.
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?
Single sentence of 12 words, concise and to the point. Front-loaded with action. However, lacks structural elements like bullet points.
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 4 parameters, 0 required, no output schema, and no annotations, the description is insufficient. It does not explain return format, error handling, or pagination details.
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 description coverage is 50% (user_id and event_type have descriptions). The description merely summarizes optional filtering but adds no new meaning. Limit/offset parameters have no textual explanation in schema or description.
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 tool lists events and mentions optional filtering by user or event type. It distinguishes from sibling list tools by specifying 'analytics events'. However, it could be more precise about the scope.
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 on when to use this tool versus alternatives like list_orders or search_knowledge_base. The description does not mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_ordersAInspect
List mock orders with optional pagination and status filter.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many orders to return. Max 100. | |
| offset | No | Starting offset for pagination. | |
| status | No | Filter by order status. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It states the tool lists 'mock orders' and supports pagination and filtering, but does not disclose if this is a read-only operation, any authorization requirements, or pagination limits beyond the schema. The term 'mock' hints at test data, which is useful.
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?
Single, well-frontloaded sentence with no unnecessary words. Every element ('List mock orders', 'optional', 'pagination and status filter') adds value.
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?
With no output schema, the description should hint at the return format (e.g., list of order objects) or pagination behavior (e.g., total count). It does not. However, it adequately covers the input purpose and basic behavior for a simple list tool.
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%, so baseline is 3. The description adds high-level context ('optional pagination and status filter') but does not enrich the meaning of individual parameters beyond what the schema already provides via their descriptions.
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?
Description clearly states the verb 'List' and the resource 'mock orders', immediately distinguishing it from siblings like 'create_order' and 'get_order'. It adds scope with 'optional pagination and status filter'.
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 implies this is the tool for listing orders, but provides no explicit guidance on when to use alternatives (e.g., 'get_order' for a single order) or when not to use. No when-to-use or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_productsAInspect
List mock products with optional pagination. Deterministic fake data.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many products to return. Max 100. | |
| offset | No | Starting offset for pagination. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry behavioral info. It states 'Deterministic fake data', implying no side effects, but does not explicitly confirm read-only or idempotency, or mention rate limits or auth.
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?
Extremely concise with two sentences. Front-loaded with action and resource. No redundant words or fluff.
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 simplicity of the tool (2 params, no output schema), the description covers the core purpose and pagination feature. It could add detail about what a product contains, but not necessary for basic selection.
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?
Input schema covers 100% of parameters with descriptions for limit and offset. The description adds 'optional pagination' which aligns with the schema, but no extra semantic meaning beyond that.
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 'List mock products with optional pagination', specifying a verb (List), a resource (mock products), and key features. It distinguishes from siblings like get_product which retrieves a single product.
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 on when to use this tool versus alternatives such as search_knowledge_base or list_orders. Not stated when not to use it or what the trade-offs are.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_usersAInspect
List mock users with optional pagination. Returns a deterministic set of fake users useful for prototyping agents.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many users to return. Max 100. | |
| offset | No | Starting offset for pagination. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description clearly states the tool returns deterministic fake data and supports pagination. It does not mention auth or rate limits, but for a read-only prototyping tool, transparency is sufficient.
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 succinct sentences front-load the purpose and provide key details. No extraneous information.
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 no output schema and simple parameters, the description adequately covers what the tool does, its data nature, and pagination. A more detailed return shape description could improve completeness.
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% and the description adds minimal value by mentioning 'optional pagination'. The schema itself already provides clear descriptions for limit and offset.
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 lists mock users with optional pagination, and specifies it's for prototyping. This distinguishes it from sibling tools like list_events and get_user.
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 implies usage for prototyping but does not explicitly state when to use this tool over alternatives like get_user or search_knowledge_base. No exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_knowledge_baseAInspect
Search a mock knowledge base. Returns fake articles ranked by a pseudo-relevance score.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many articles to return. Max 25. | |
| query | Yes | Search query. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It honestly discloses that the knowledge base is mock and articles are fake with a pseudo-relevance score, which sets accurate expectations. It does not detail permissions or rate limits, but the mock nature reduces the need for deep behavioral traits.
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 exceptionally concise: two sentences that immediately convey the tool's purpose and a key behavioral note. Every word earns its place, with no redundant or extraneous content.
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 that no output schema or annotations exist, the description is minimal. It covers the purpose and mock nature, but lacks details on response format or advanced usage nuances (e.g., how ranking works). For a simple search, it is adequate but not fully comprehensive.
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%, and the schema already describes query and limit. The description adds context about the pseudo-relevance ranking but does not explain parameter semantics beyond what the schema provides. Baseline 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 tool searches a mock knowledge base and returns fake articles ranked by a pseudo-relevance score. This verb+resource combination distinguishes it from sibling tools, which are CRUD and email operations.
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 provides implicit context (search use case) but no explicit when-to-use or when-not-to-use guidance. Since no other search tool exists among siblings, the usage is apparent, but the description misses opportunities to clarify limits or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
send_emailAInspect
Send a mock email. Nothing is actually delivered — returns a success response with a generated message ID.
| Name | Required | Description | Default |
|---|---|---|---|
| cc | No | ||
| to | Yes | Single recipient or array of recipients. | |
| bcc | No | ||
| body | Yes | Plain text or HTML body. | |
| from | No | ||
| subject | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description explicitly states that nothing is actually delivered and it returns a success response with a message ID. This is good behavioral disclosure given no annotations. However, it could mention that it is safe for testing.
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 sentences, no superfluous information. Efficiently communicates the essential behavior.
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?
Adequately explains return value (success response with message ID) for a mock tool. Lacks error handling details or usage context among siblings, but acceptable for a simple test utility.
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?
Only 33% of parameters have descriptions in the schema, and the description does not add any extra meaning beyond what the schema provides. No constraints or formats are clarified for parameters like subject, from, cc, bcc.
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?
Clearly states it sends a mock email that does not actually deliver, and returns a success response with a generated message ID. The verb 'send' and object 'email' are specific, distinguishing it from sibling tools.
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 on when to use this mock tool versus alternatives. Sibling tools include various CRUD operations, but no mention of real email functionality or when to choose this over them.
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!
Your Connectors
Sign in to create a connector for this server.