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Server Quality Checklist

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Latest release: v1.0.3
Disambiguation4/5
Most tools have distinct purposes, but execute_code and execute_workflow could be confused without reading descriptions. suggest_approach and usage_guide also overlap slightly in guiding the user.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, with no mixing of conventions.
Tool Count5/5
9 tools is appropriate for a meta-orchestration server, covering listing, searching, execution, and guidance without being overwhelming.
Completeness4/5
The tool surface covers key actions like executing code/workflows, discovering resources, and getting help. A tool to view workflow status or cancel executions would make it more complete.
Average 3.7/5 across 9 of 9 tools scored. Lowest: 2.7/5.
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
- Last stable release on November 26, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT 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.
This repository includes a glama.json configuration file.
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.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavioral traits. It only states the effect but does not mention side effects, persistence, scoping, or state changes. Minimal transparency.
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 very short and front-loaded, but it sacrifices necessary detail. While concise, it is under-specified for a meaningful tool definition.
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?
With one required parameter, no output schema, and no annotations, the description is incomplete. It lacks details on behavior, return values, or impact on other tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'path' has no schema description coverage (0%), and the description adds no additional meaning beyond its name. No clarification on path format, validity, or constraints.
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 action ('set') and the resource ('project root') along with its purpose ('for path resolution'). It distinguishes itself from sibling tools like execute_code or get_tool_api.
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 on when to use this tool vs alternatives, nor any conditions or exclusions. The description is silent on usage context.
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. It states the tool returns schemas but does not disclose whether it is read-only, any side effects, permissions, or rate limits, lacking essential 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 exceptionally concise with two sentences, providing an imperative instruction and a clear statement of output, with no extraneous words.
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 simple tool (1 param, no output schema), the description is minimally adequate, explaining usage and return value but lacking detail on the parameter and output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fails to explain the serverName parameter, not specifying its source or format. The description's mention of 'server' does not add concrete parameter semantics.
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 returns tools with input requirements for a server, with an imperative instruction to call before using a server. It distinguishes purpose from siblings like list_servers and search_tools, though not explicitly naming them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises calling before using a server, which implies when to use, but does not specify when not to use or provide alternatives, leaving usage context somewhat vague.
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 must disclose behaviors. It mentions parallel execution and automatic retries, which is helpful, but lacks details on side effects, whether the operation is read-only or destructive, response format, or potential delays. This is a minimal disclosure for a tool that likely triggers actions.
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?
Two sentences with no wasted words. The first sentence front-loads usage guidance (USE WHEN), and the second adds behavioral traits. Structurally efficient.
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 no output schema, the description should hint at return values or behavior. It fails to mention output type, error handling, or asynchronous nature. While it covers purpose and basic automation features, it is incomplete for safe agent usage.
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 baseline is 3. The description does not add additional meaning beyond what the schema provides (e.g., 'Workflow-specific parameters' is already in the schema). No extra value is contributed.
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 by specifying three use cases (research, K8s auditing, incident analysis) and mentions pre-built automation. It distinguishes from siblings like 'list_workflows' and 'execute_code' through context, though it could be more explicit about the action (executing a workflow).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'USE WHEN' for three domains, providing clear context. However, it does not mention when not to use this tool or suggest alternatives among siblings, which would improve 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 must carry the full burden of behavioral disclosure. It states the tool 'searches across all loaded MCP servers' but does not mention whether it modifies state, requires specific permissions, or the effect of parameters like 'hydrateLazy'. Given the lack of annotations, more detail on side effects and behavior is needed.
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: two sentences front-loading the usage condition and action. Every sentence earns its place with no redundancy. It is well-structured for quick parsing.
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?
The tool has four parameters and no output schema. The description does not explain the return format (e.g., list of tool names, descriptions, or full details) despite the search function implying a return of matching tools. This gap leaves the agent uncertain about what to expect, making it incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 50% description coverage, meaning two of four parameters lack descriptions. The tool description adds no parameter-specific information; it only mentions searching by name/description, which loosely relates to the 'query' parameter but does not clarify format or behavior. The description fails to compensate for the missing schema descriptions.
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 tool's purpose: searching across MCP servers for tools when the server is unknown. It includes the scope ('all loaded MCP servers') and the search criteria ('by name/description'), effectively distinguishing it from sibling tools like 'list_servers' and 'get_tool_api'.
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 provides a usage condition ('USE WHEN you don't know which server has the tool you need'), guiding the agent on when to invoke this tool. It does not explicitly mention when not to use, but the context signals and sibling names imply alternatives, making the guidance clear enough.
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 exist, so description carries full burden. It does not disclose whether the tool is read-only, has side effects, or requires permissions. Minimal behavioral context beyond the recommendation claim.
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?
Extremely concise (3 lines), front-loaded with key usage signal ('CALL WHEN UNSURE'). Every sentence adds value without redundancy.
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?
Covers core purpose and key considerations but lacks output details (no output schema). Only addresses three of eight sibling tools, missing guidance for alternatives like search_tools or list_workflows.
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 covers 100% of parameters, so baseline is 3. Description adds marginal context by mentioning considerations (e.g., operation count, data size) that loosely map to parameters, but does not specify how each parameter influences output.
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 explicitly states the tool's purpose: analyzing tasks to recommend between execute_code, execute_workflow, or direct MCP. It uses specific verbs ('analyzes', 'recommends') and distinguishes from sibling tools by specifying when to call it.
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?
Provides clear 'call when unsure' condition and lists considerations (operation count, data size, existing workflows). Lacks explicit exclusions or alternatives beyond the three mentioned tools, but guidance is strong.
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 full burden. It states the tool returns guides, implying a read-only operation. However, it does not disclose potential side effects, error handling, or parameter dependency beyond what is implied.
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 sentence conveying purpose and usage, with no redundant information. It is appropriately front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with one optional parameter and no output schema, the description covers the basic intent and when to use. It could mention the parameter's purpose, but the schema fills that gap, so it is nearly complete.
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 enum values, so the schema already defines parameter semantics. The description does not add any additional meaning about the 'section' parameter beyond what the schema provides.
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 returns step-by-step guides for CodeModeTOON when confused. It uses specific verb 'returns' and resource 'guides', distinguishing it from siblings like 'search_tools' which search instead of providing guides.
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 says 'CALL WHEN confused about CodeModeTOON', providing clear context for use. It does not specify when not to use or mention alternatives, but the directive is strong enough for an agent.
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 provided, so description carries full burden. It mentions TOON compression default and error recovery patterns (timeout, server not found), but lacks warnings about security implications or side effects of executing arbitrary code.
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?
Well-structured with headings, but overly long. Includes extensive workflow descriptions for other tools that are not directly relevant, reducing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers when to use, not to use, usage pattern, and error recovery. Lacks security warnings and return value details, but given the single parameter and no output schema, it is reasonably complete.
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?
Schema coverage is 100% and schema description already explains the 'code' parameter. The description adds a usage pattern example that shows how to call MCP tools via proxy, adding practical context beyond the schema.
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?
Description clearly states the tool executes TypeScript/JavaScript code for batching MCP calls and processing data. It distinguishes from sibling execute_workflow by noting that specific workflows should use that tool instead.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists when to use (batching 3+ calls, large data, complex logic) and when not to (single call, low-compression prose). Also mentions alternatives like execute_workflow for specific workflows.
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 full burden. It describes a read-only listing with no side effects, but does not explicitly confirm non-destructiveness or address potential permissions. For a simple listing, this is adequate but not exhaustive.
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?
Extremely concise: two sentences, no redundant words. The first sentence gives directed action, the second adds useful detail. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description explains what categories of servers are shown (loaded, lazy, disabled). For a simple listing tool with no parameters, this is complete enough for an agent to understand the result.
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?
There are no parameters and schema coverage is 100%. The description adds no parameter semantics, but none are needed. Baseline of 4 for zero-parameter tools is appropriate.
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?
Clearly states verb 'list' and resource 'servers', and specifies that it shows loaded, lazy, and disabled servers. Differentiates from sibling tools like list_workflows by emphasizing 'CALL FIRST' as a precursor step.
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?
Provides explicit usage instruction 'CALL FIRST', indicating it should be used before other tools. Does not explicitly mention when not to use or alternatives, but the instruction is strong enough to guide correct invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description conveys read-only behavior by listing returned data. It does not mention limitations like pagination, but the tool has no parameters, so the description 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff, front-loaded with the most important directive 'CALL FIRST.' Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
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 needed), the description fully covers what an agent needs to know to decide to use it.
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?
Input schema has zero parameters and 100% coverage, so description does not need to add parameter details. Baseline 4 is appropriate.
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 it 'discovers available automations' and returns 'workflow names, descriptions, and required parameters,' which is specific and distinct from sibling tools like execute_workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'CALL FIRST to discover available automations before writing custom code,' providing clear guidance on when to use it and implying it should precede other actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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