Free platform to test MCP clients without installing anything. Create mock tools with dynamic templates, configurable delays, conditions (if/then), and response sequences. Supports JSON-RPC 2.0 over Streamable HTTP. Built-in text_echo and json_echo tools. Rate-limited tiers: anonymous (5 calls/min, 1 mock tool), registered (10 calls/min, 4 mock tools), premium (60 calls/min, unlimited). Zero setup — no install, no registration required. More info: https://www.testmcp.dev
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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.
Average 4/5 across 2 of 2 tools scored. Lowest: 3.4/5.
The two tools target distinct input types (JSON vs plain text), so there is no ambiguity in choosing between them.
Both tools follow a consistent verb_noun pattern (json_echo, text_echo), making the naming predictable.
With only 2 tools, the set is minimal but appropriate for a simple echo service; however, it feels thin for a general-purpose server.
The server covers the two main input types (JSON and plain text) for an echo utility, leaving no obvious gaps within its narrow scope.
json_echoJSON EchoAInspectReceives JSON and returns it as echo (max 256 chars serialized for Free tier, up to 100kb for Premium).
| Name | Required | Description | Default |
|---|---|---|---|
| json_data | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description effectively discloses the echo behavior and size constraints. It is clear that the tool is read-only and returns the input. A slightly higher score would require explicit mention of no side effects or authentication needs.
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 is efficient and front-loaded with the core purpose. Every word is necessary, and there is no redundant 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?
For a simple echo tool with one parameter, the description covers purpose and size limits. It does not explain what 'echo' means in detail, but given the presence of an output schema, that is acceptable. Minor improvement could clarify the echo behavior.
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 description does not reference the only parameter, json_data. With 0% schema description coverage, the description should provide additional context but fails to do so. The parameter name is self-explanatory, but the description adds no semantic value.
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 receives JSON and returns it as echo, identifying the core purpose. However, it lacks an explicit statement that it is a testing/debugging tool, and sibling differentiation is implied rather than explicit.
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 size limits for Free vs Premium tiers, which gives context, but does not explicitly guide when to use json_echo over text_echo. The differentiation is implied by the input type.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
text_echoText EchoAInspectReceives plain text (max 256 chars for Free tier, up to 100kb for Premium) and returns it as echo.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 clearly states that the tool echoes back the input text and discloses character limits by tier. This covers key behavioral traits such as input size constraints and nondestructive echo behavior, though it could mention if it’s idempotent or has other side effects.
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, front-loaded with the tool’s purpose, and every part is necessary. 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?
Given the tool has an output schema (not shown but true), the description does not need to explain return values. It covers input constraints (size limits) and the basic echo behavior, which is sufficient for such a simple tool. The tool's low complexity and presence of output schema make this description 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%, so the description must compensate. It implies the 'text' parameter by mentioning 'plain text' and adds meaning with size limits. However, it does not explicitly define the parameter name or type beyond what the schema provides. This adds some value but not full compensation.
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 receives plain text and returns it as an echo. This distinguishes it from the sibling tool 'json_echo', which presumably handles JSON input. The verb 'returns' and the specific resource 'text echo' are well-defined.
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 tier-based size limits (256 chars for Free, 100kb for Premium), offering context on when the tool can be used. However, it does not explicitly state when to prefer this tool over 'json_echo' or provide exclusion guidance. The absence of explicit 'when-not-to-use' notes keeps it from a 5.
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.
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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
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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:
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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.
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