Multi-MCP

by religa

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose: general chat, code review, model response comparison, multi-model debate, listing models, and server version. No overlap between tools.
Naming Consistency4/5
All tool names are single, lowercase words, which is consistent in style. However, part of speech varies (e.g., 'compare' is a verb while 'models' is a noun), which is a minor inconsistency.
Tool Count5/5
With 6 tools, the server covers its intended multi-model AI interaction scope concisely without being bloated or insufficient.
Completeness5/5
The tool surface covers the full range of expected operations: general interaction, code review, model comparison, debate, and informational queries. No obvious gaps in functionality.
Average 3.6/5 across 6 of 6 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 8 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
With no annotations, the description must fully disclose behavioral traits. It mentions multi-turn conversation support and file inclusion but omits important details such as whether the tool modifies state, requires authentication, has rate limits, or how conversation history is managed. The agent may not know that it can safely invoke this tool without 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief at two sentences, front-loading the core purpose. It avoids verbosity but could include more useful information (e.g., tip on when to use) without becoming too long. It is appropriately sized for a simple chat tool.
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 8 parameters (5 required), no annotations, and an output schema, the description is insufficient. It fails to explain the multi-turn workflow, how to start vs. continue a conversation, or what the output contains. The schema descriptions partly compensate, but the description should provide higher-level guidance.
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?
All 8 parameters are fully described in the input schema, so the baseline is 3. The description adds minimal value by framing parameters in terms of 'project context' and 'file inclusion,' but the schema already covers this in detail. There is no significant additional semantic guidance.
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 states 'General chat with AI assistant' which clearly identifies the tool's purpose. It further specifies multi-turn conversations and project context, distinguishing it from sibling tools like codereview and compare. However, it could be more explicit about the scope of the chat (e.g., programming assistance vs. general Q&A).
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. Sibling tool names are given but without any context or comparison. The description does not mention when not to use chat or suggest other tools for specific tasks.
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 clarify behavior. It only says 'uses external models' without detailing model behavior, result handling, or limitations. The multi-step process and requirement for cumulative issues_found are not mentioned, leaving significant gaps.
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 concise at one sentence, but it lacks structure and misses key contextual information. It is not overly verbose, but it could be better organized to front-load important details.
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 9 parameters and the step-based workflow implied by parameters like step_number and thread_id, the description provides insufficient high-level context. It does not explain the review process or how results are aggregated, relying too heavily on parameter descriptions.
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 itself explains parameters well. The description does not add new semantic meaning beyond the schema, so it meets the baseline without excelling.
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 it performs code review covering quality, security, performance, and architecture, distinguishing it from sibling tools like chat or debate. However, it omits mentioning the multi-step workflow evident from the input schema, which would strengthen clarity.
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 implies the tool is for code review but provides no explicit guidance on when to use it versus alternatives, nor does it mention when not to use it. Sibling tool names suggest different purposes, but the description itself lacks directives.
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?
With no annotations, the description carries full burden. It discloses the two-step behavioral flow (independent answers then debate/voting), which is useful. However, it omits details like idempotency, side effects, or any constraints. Adequate but not thorough.
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 clear sentences, no redundant words. Front-loaded with the core concept 'Multi-model debate'. Every sentence adds value, efficiently conveying the two-step process.
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 full schema coverage and presence of an output schema, the description adequately covers the conceptual flow. It could mention the requirement of at least two models, but that is already in the schema. Minor gaps exist but overall sufficient for an agent to understand execution.
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 tool description does not add any parameter-specific meaning beyond what the schema already provides. It focuses on the overall process rather than individual parameters.
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 'Multi-model debate' and outlines the two-step process (independent answers then debate/critique), which clearly sets it apart from sibling tools like chat (single model) or compare (likely pairwise comparison). The verb+resource is specific and distinct.
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 explicit guidance on when to use this tool vs alternatives. It describes the process but does not specify scenarios (e.g., when you need multiple perspectives vs a single answer) or provide exclusion criteria. Lacks context for appropriate use.
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?
The description implies idempotent read behavior by stating 'list available AI models', but it does not explicitly confirm no side effects or mention idempotency. With no annotations, the description carries full burden; it is adequate but not comprehensive.
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 states the core purpose; the second specifies return values. Front-loaded and efficient.
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 zero parameters and a simple listing task, the description covers purpose and return fields adequately. The presence of an output schema (even if not shown) means return details can be inferred, so no further explanation is needed.
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?
No parameters exist, so schema coverage is 100%. The description does not need to add parameter details. Baseline 4 is appropriate as it does not detract.
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 'lists available AI models' using a specific verb and resource. It further specifies return fields (names, aliases, provider, configuration), making the purpose explicit and distinguishing it from sibling tools like 'chat' or 'compare' which are interactive.
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 versus alternatives. Sibling tools exist (chat, codereview, etc.) but the description does not mention them or provide context for selecting this tool. For a simple listing, this omission is less critical but still a gap.
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?
Discloses parallel execution, multi-turn conversation support, and file inclusion. With no annotations, description provides good behavioral context, though no mention of side effects or permissions.
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?
Three concise sentences, each adding essential information. No redundancy or filler. Front-loaded with core action.
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 8 parameters and output schema exists, description covers key aspects. Could mention output format but not required. Adequate for a comparison 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 covers all 8 parameters with descriptions. Description adds that content is sent to all models in parallel, but this is implicit from the tool's purpose. Baseline 3 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?
Description clearly states the tool compares responses from multiple AI models, running same content in parallel. Distinguishes from siblings like chat and codereview by emphasizing multi-model comparison.
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?
Does not explicitly specify when to use vs alternatives like debate or chat. Mentions multi-turn support but lacks guidance on choosing this tool over others.
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?
The description implies a read-only operation by stating 'Get', which combined with no parameters and output schema suggests no side effects. However, without annotations, explicit disclosure of non-destructiveness or authorization needs would be beneficial.
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?
A single 10-word sentence that is front-loaded with the verb. Every word is necessary and there is no redundancy.
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?
For a simple tool with no parameters and an output schema, the description covers the key return aspects (version, configuration, tool list). The presence of an output schema further supports completeness.
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, so baseline score is 4. The description doesn't need to add parameter information beyond what the empty schema provides.
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 verb 'Get' and the resources: server version, configuration details, and list of available tools. This is specific and distinguishes from sibling tools like chat, codereview, compare, debate, models.
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 versus alternatives. It's a simple info retrieval tool, but no explicit context or exclusion is provided.
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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