MCP Elicitations Demo Server

Overview Schema Related Servers Score Discussions

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation3/5
The tools have distinct technical purposes, but there is some functional overlap in the demonstration category. For example, 'annotatedMessage', 'longRunningOperation', 'sampleLLM', and 'startElicitation' all serve as MCP feature demos, which could cause confusion about which to use for general demonstration tasks. However, their specific descriptions help differentiate them.
Naming Consistency2/5
The naming conventions are inconsistent, mixing verb-first (e.g., 'add', 'echo', 'printEnv'), noun-first (e.g., 'annotatedMessage'), and descriptive phrases (e.g., 'longRunningOperation', 'getResourceLinks'). There is no uniform pattern like verb_noun or camelCase throughout, making the set less predictable.
Tool Count4/5
With 10 tools, the count is reasonable for a demo server showcasing various MCP features. It is well-scoped to demonstrate capabilities without being overwhelming, though it might feel slightly heavy if the purpose is purely educational rather than functional.
Completeness5/5
As a demo server, the tool set comprehensively covers the intended domain of MCP feature demonstrations, including basic operations, resource handling, long-running tasks, LLM sampling, and elicitations. There are no obvious gaps for its stated purpose of showcasing MCP functionalities.
Average 2.9/5 across 10 of 10 tools scored. Lowest: 2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Inno Setup License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but fails completely. It doesn't indicate whether this is a read-only operation, whether it has side effects, what authentication might be required, or any rate limits. The single sentence provides no behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - just three words - but this brevity comes at the cost of being under-specified rather than efficiently informative. While it's front-loaded with the core action, it lacks the necessary explanatory content that would make it genuinely helpful to an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations, no output schema, and the description provides minimal information, this is incomplete for agent understanding. The tool name suggests it retrieves some kind of image resource, but the description doesn't explain what 'MCP_TINY_IMAGE' represents, what format it returns, or why one would use this versus other resource-fetching tools on the server.
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?
The tool has zero parameters, and the input schema has 100% description coverage (though empty). The description appropriately doesn't waste space discussing nonexistent parameters. With no parameters to document, the baseline score of 4 reflects that the description doesn't need to compensate for any parameter documentation gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Returns the MCP_TINY_IMAGE' is a tautology that essentially restates the tool name 'getTinyImage' with minimal additional information. While it does specify the verb 'Returns' and resource 'MCP_TINY_IMAGE', it doesn't explain what this resource represents or what purpose it serves, making it vague rather than specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance about when to use this tool versus alternatives. There's no mention of context, prerequisites, or comparison with sibling tools like 'getResourceLinks' or 'getResourceReference' that might serve similar purposes. This leaves the agent with no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states it 'demonstrates how annotations can be used', which doesn't reveal whether this is a read-only operation, if it has side effects, what it returns, or any performance characteristics. For a tool with no annotation coverage, this leaves critical behavioral traits unspecified.
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?
The description is a single, straightforward sentence that efficiently conveys its core idea without unnecessary words. It's appropriately sized for a demonstration tool, though it could be more front-loaded with actionable information about what the tool actually produces or does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a sample annotated message, a demonstration output), how the parameters affect the demonstration, or any educational context. For a tool with 2 parameters and no structured output documentation, more detail is needed to make it fully usable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters ('includeImage' and 'messageType'), including an enum for messageType. The description adds no parameter-specific information beyond what the schema already provides. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'demonstrates how annotations can be used to provide metadata about content', which gives a vague purpose but doesn't specify what the tool actually does operationally. It mentions 'annotations' and 'metadata' but lacks a clear verb+resource combination. Compared to siblings like 'echo', 'add', or 'getResourceLinks', it doesn't clearly differentiate its specific function beyond being a demonstration tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any specific contexts, prerequisites, or exclusions. Given siblings like 'echo' (for echoing input) or 'sampleLLM' (for LLM sampling), there's no indication of when this demonstration tool would be preferred over others for testing or educational purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns multiple resource links but doesn't explain what a 'resource link' is, how they are selected, whether there are permissions or rate limits, or what the output format looks like. This leaves significant gaps in understanding the tool's behavior.
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?
The description is a single sentence that is efficient and front-loaded, stating the core purpose without unnecessary words. However, it could be more structured by including key details like parameter context or usage distinctions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of returning 'resource links' with no annotations or output schema, the description is incomplete. It fails to clarify what resource links are, how they differ from sibling tools, or what the return values entail, making it inadequate for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'count' parameter clearly documented in the schema. The description adds no additional meaning beyond what the schema provides, as it doesn't mention parameters at all. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Returns multiple resource links that reference different types of resources', which provides a basic purpose but is vague about what 'resource links' are and what 'different types of resources' means. It doesn't clearly distinguish this tool from sibling tools like 'getResourceReference' or 'getTinyImage', leaving ambiguity about when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions, and it doesn't reference sibling tools like 'getResourceReference' to help differentiate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns something but doesn't explain what a 'resource reference' entails (e.g., format, content, or how it's used by MCP clients), whether it's a read-only operation, or any potential side effects. This is inadequate for a tool with no annotation coverage, as it leaves key behavioral traits unspecified.
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?
The description is a single, efficient sentence that states the core function without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly. Every part of the sentence contributes to the purpose, earning its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a tool that returns a reference with no output schema) and lack of annotations, the description is incomplete. It doesn't explain what the returned 'resource reference' is, how it's structured, or its use cases, leaving gaps in understanding the tool's behavior and output. This is insufficient for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter semantics beyond what the input schema provides. The schema has 100% description coverage, clearly documenting the 'resourceId' parameter with its type, range, and requirement. Since the description doesn't elaborate on parameters, it meets the baseline of 3 for high schema coverage without adding value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Returns a resource reference that can be used by MCP clients', which provides a basic purpose (verb 'returns' + object 'resource reference'). However, it's vague about what a 'resource reference' actually is and doesn't distinguish this from sibling tools like 'getResourceLinks' or 'getTinyImage' that might also return references or resources. The purpose is stated but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 versus alternatives. The description doesn't mention any context, prerequisites, or exclusions, and it doesn't reference sibling tools. This leaves the agent with no information on appropriate usage scenarios, making it rely solely on the tool name and input schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'long running operation' and 'progress updates', which hints at asynchronous behavior and status reporting, but doesn't specify timeout expectations, cancellation support, error handling, or what 'progress updates' actually entail. This leaves significant behavioral gaps for a tool explicitly about long-running operations.
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?
The description is a single, efficient sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration. It's appropriately sized for what it communicates.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool about long-running operations with no annotations and no output schema, the description is insufficient. It doesn't explain what the operation actually does, what progress updates look like, how results are returned, or any error conditions. The agent lacks critical context needed to properly use this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (duration and steps). The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'demonstrates a long running operation with progress updates', which provides a general purpose but lacks specificity about what resource or domain it operates on. It distinguishes from siblings by mentioning 'long running' and 'progress updates', but doesn't specify what exactly is being operated on or demonstrated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, constraints, or comparison to sibling tools. The agent must infer usage purely from the name and description without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'sampling' but doesn't disclose behavioral traits like whether it's read-only or mutative, potential rate limits, authentication needs, or what the output format is (e.g., text, tokens). This is inadequate for a tool with no annotation coverage.
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?
The description is a single, efficient sentence with no wasted words. It's appropriately sized and front-loaded, clearly stating the core purpose without unnecessary elaboration, earning a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of LLM sampling, no annotations, and no output schema, the description is incomplete. It lacks details on behavior, output format, and usage context, which are crucial for an agent to effectively invoke this tool. It should provide more guidance to compensate for the missing structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the two parameters ('prompt' and 'maxTokens'). The description adds no meaning beyond this, such as explaining how sampling interacts with the prompt or token limits. Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Samples from an LLM using MCP's sampling feature', which provides a basic verb+resource ('samples from an LLM') but lacks specificity about what sampling entails (e.g., generating text, completing prompts) and doesn't distinguish it from sibling tools like 'annotatedMessage' or 'echo' that might involve LLM interactions. It's vague but not tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, such as when to prefer 'annotatedMessage' or 'echo' for similar tasks. This leaves the agent without direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. 'Adds two numbers' implies a simple calculation but reveals nothing about error handling (e.g., overflow, invalid inputs), performance characteristics, or what the output looks like. For a tool with zero annotation coverage, this is insufficient behavioral context.
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?
The description is maximally concise with just three words that directly convey the core functionality. There is zero wasted language, and the information is front-loaded appropriately for such a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (basic arithmetic operation with 2 parameters) and 100% schema coverage, the description is minimally complete. However, the lack of output schema means the description should ideally mention what is returned (e.g., a sum), but doesn't. The absence of annotations also leaves behavioral gaps unaddressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters clearly documented as 'First number' and 'Second number'. The description adds no additional parameter semantics beyond what the schema already provides. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even without parameter info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Adds') and resource ('two numbers'), making the purpose immediately understandable. It doesn't differentiate from siblings, but since none of the sibling tools appear to be arithmetic operations, this is adequate. The description avoids tautology by specifying what is being added.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, constraints, or suggest other tools for different mathematical operations. While the sibling list doesn't include obvious alternatives like 'subtract' or 'multiply', the lack of any usage context leaves the agent without guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. 'Echoes back' implies a simple read operation, but it doesn't address potential side effects, error conditions, or output format. For a tool with zero annotation coverage, this leaves significant behavioral 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?
The description is extremely concise with just three words, front-loading the core functionality ('Echoes back') without any wasted text. Every word earns its place in communicating the essential purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is minimally adequate but incomplete. It doesn't explain what 'echoing back' entails (e.g., format, transformations) or address the lack of annotations, leaving the agent with unanswered questions about behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'the input' which aligns with the single 'message' parameter, but adds no semantic meaning beyond what the schema already provides (schema description coverage is 100%). This meets the baseline for high schema coverage without enhancing parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Echoes back') and resource ('the input'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'sampleLLM' or 'printEnv' which might also involve returning input, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'sampleLLM' or 'printEnv' that might serve similar testing/debugging purposes, the agent receives no explicit or implied context about appropriate usage scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool asks for user input (color, number, pets), implying an interactive or input-gathering behavior, but doesn't describe how this elicitation works (e.g., prompts, format, response handling), whether it's read-only or mutative, or any side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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?
The description is a single, efficient sentence: 'Demonstrates the Elicitation feature by asking the user to provide information about their favorite color, number, and pets.' It's front-loaded with the main purpose and includes necessary details without waste. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, no annotations, and no output schema, the description provides basic context about what the tool does (eliciting user info). However, it lacks details on behavior, output, or integration with sibling tools, making it incomplete for full understanding. It's adequate as a minimal description but has clear gaps in explaining how the tool operates or what to expect.
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?
The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details since there are none to explain, which is appropriate. Baseline is 4 for 0 parameters, as the description doesn't need to compensate for missing schema info and aligns with the schema's emptiness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Demonstrates the Elicitation feature by asking the user to provide information about their favorite color, number, and pets.' It specifies the verb ('demonstrates') and the resource/feature ('Elicitation feature'), and explains what the tool does (asks for specific user information). However, it doesn't explicitly differentiate from sibling tools like 'annotatedMessage' or 'sampleLLM' that might also involve user interaction, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions it 'demonstrates the Elicitation feature,' which implies a demo or testing context, but doesn't specify when to choose it over other tools like 'annotatedMessage' for user input or 'sampleLLM' for AI interaction. There's no mention of prerequisites, exclusions, or explicit alternatives, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 tool's behavior (prints all environment variables) and its purpose (debugging), but lacks details like output format, security implications (e.g., exposing sensitive data), or performance characteristics. This is adequate but has 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?
The description is a single, efficient sentence that front-loads the core action ('Prints all environment variables') and adds value with the debugging context. Every word earns its place with no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is complete enough for basic use. However, it lacks details on output format (e.g., structured vs. plain text) and potential risks (e.g., exposing secrets), which could be important for debugging scenarios.
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?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately does not discuss parameters, earning a high baseline score for not adding unnecessary information.
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 specific action ('Prints') and resource ('all environment variables'), and distinguishes its debugging purpose from sibling tools like 'echo' or 'getResourceLinks' which serve different functions. It goes beyond a tautology by explaining what gets printed.
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 states when to use this tool ('helpful for debugging MCP server configuration'), providing clear context. However, it does not specify when not to use it or name alternatives among siblings, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![soria-mcp MCP server](https://glama.ai/mcp/servers/soriat/soria-mcp/badges/card.svg)](https://glama.ai/mcp/servers/soriat/soria-mcp)

Score Badge

Copy to your README.md:

[![soria-mcp MCP server](https://glama.ai/mcp/servers/soriat/soria-mcp/badges/score.svg)](https://glama.ai/mcp/servers/soriat/soria-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/soriat/soria-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server