Synergy/DE MCP Server

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: describe_docs for metadata, get_related_topics for topic relationships, get_topic for fetching specific content, list_section_topics for section overviews, and search_docs for full-text search. An agent can easily distinguish between them based on their names and descriptions.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case: describe_docs, get_related_topics, get_topic, list_section_topics, and search_docs. The verbs (describe, get, list, search) are appropriate and predictable, making the set easy to navigate and understand.
Tool Count5/5
With 5 tools, the server is well-scoped for documentation access, covering metadata retrieval, topic fetching, section listing, relationship discovery, and search. Each tool earns its place without redundancy, aligning with typical MCP server sizes of 3-15 tools for focused domains.
Completeness5/5
The tool set provides complete coverage for documentation interaction: describe_docs for overview, list_section_topics and search_docs for discovery, get_topic for content retrieval, and get_related_topics for navigation. There are no obvious gaps, enabling agents to perform full documentation workflows without dead ends.
Average 3.2/5 across 5 of 5 tools scored.
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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral context. It states what relationships are returned but doesn't disclose permissions, rate limits, error conditions, or response format. For a read operation, this leaves significant gaps in understanding how the tool behaves.
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 purpose. Every word contributes to understanding the tool's function without redundancy or unnecessary elaboration.
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 annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., structure of related links), error handling, or dependencies. For a tool with two parameters and unknown output, more context is needed for effective 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?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no parameter-specific information beyond implying 'topic_id' identifies the topic for which relationships are fetched. This meets the baseline for high schema coverage.
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 'Get' and the resource 'related topics', specifying the types of relationships (previous, next, parent, and related links). It distinguishes from siblings like 'get_topic' (which presumably gets topic content) and 'list_section_topics' (which lists topics in a section), but doesn't explicitly contrast them.
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 like 'get_topic' or 'list_section_topics'. The description implies usage when navigation or contextual relationships are needed, but lacks explicit when/when-not instructions or prerequisite 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?
With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose if it's read-only, requires authentication, has rate limits, returns structured data, or handles errors. This leaves significant behavioral gaps for an agent.
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 ('Get metadata') and specifies the resource and metadata types. There is no wasted verbiage or unnecessary elaboration.
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 annotations and no output schema, the description is incomplete. It doesn't explain what the metadata output looks like (e.g., format, structure), potential limitations, or how it integrates with sibling tools. For a tool with zero parameters but unknown output, more context 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?
The tool has 0 parameters, and schema description coverage is 100% (though empty). The description doesn't need to add parameter details, so it meets the baseline of 4 for zero-parameter tools by focusing on the tool's purpose without redundancy.
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 action ('Get metadata') and resource ('available documentation'), specifying what kind of metadata (versions, sections, source type). It distinguishes from siblings like 'search_docs' (searching content) or 'get_topic' (retrieving specific content), but doesn't explicitly contrast them.
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 like 'list_section_topics' or 'search_docs'. The description implies it's for metadata about documentation structure, but doesn't specify use cases, prerequisites, 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format (summaries with IDs, titles, URLs, and summaries), which is helpful, but lacks details on pagination (implied by 'limit'), error handling, authentication needs, rate limits, or whether it's a read-only operation. For a tool with 3 parameters and no annotations, this leaves 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.
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 ('List all topics in a documentation section') and follows with key return details. Every word earns its place, with no redundancy or unnecessary elaboration.
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 3 parameters, no annotations, and no output schema, the description is minimally adequate. It covers the purpose and return format, but lacks behavioral context (e.g., safety, errors) and doesn't fully address usage relative to siblings. For a read-like tool, it's passable but incomplete for optimal agent decision-making.
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 documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples or constraints beyond defaults). Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with extra insights.
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 ('List') and resource ('topics in a documentation section'), making the purpose immediately understandable. It distinguishes from siblings like 'get_topic' (single topic) and 'search_docs' (search across docs), though not explicitly. However, it doesn't fully differentiate from 'get_related_topics' or 'describe_docs' in scope.
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 versus alternatives like 'get_topic' for single topics or 'search_docs' for broader searches. The description implies usage for listing topics within a section but doesn't specify prerequisites, exclusions, or comparative contexts with sibling tools.
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 mentions that the tool 'returns a list of matching topics with relevance scores,' which gives some output context, but lacks details on permissions, rate limits, error handling, or whether it's a read-only operation. For a search tool with no annotations, this is a significant gap in transparency.
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 highly concise and well-structured: two sentences that efficiently cover the action and output. Every sentence earns its place by providing essential information without redundancy. It's front-loaded with the core purpose, making it easy for an agent to parse quickly.
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 moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output but lacks details on behavioral traits, usage context, and deeper parameter meaning. Without an output schema, it doesn't fully explain return values beyond mentioning 'relevance scores,' leaving gaps in completeness.
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 schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain search syntax or relevance scoring). According to the rules, with high schema coverage, the baseline is 3 even without param 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 tool's purpose: 'Search documentation topics using full-text search.' It specifies the verb ('search') and resource ('documentation topics'), and mentions the return type ('list of matching topics with relevance scores'). However, it doesn't explicitly differentiate from sibling tools like 'describe_docs' or 'get_related_topics' beyond the search functionality.
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 like 'describe_docs' or 'get_related_topics'. It states what the tool does but offers no context about when it's appropriate, such as for finding topics by keyword versus browsing sections. This leaves the agent without explicit usage direction.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a read operation ('Fetch'), returns chunked content optimized for LLMs, and implies no destructive actions. However, it doesn't mention potential errors (e.g., invalid IDs), rate limits, or authentication needs, leaving some behavioral aspects uncovered.
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 highly concise and front-loaded in a single sentence, with no wasted words. It efficiently communicates the core action, inputs, and output format, making it easy for an agent to parse and understand quickly.
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 tool's moderate complexity (4 parameters, no output schema, no annotations), the description is reasonably complete. It covers the purpose, input methods, and output format, but lacks details on error handling, performance constraints, or examples, which could enhance completeness for a retrieval 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 fully documents all four parameters. The description adds no specific parameter semantics beyond what the schema provides, such as explaining relationships between 'topic_id' and 'url' or detailing 'max_chunks' behavior. 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fetch'), resource ('documentation topic'), and method ('by ID or URL'), distinguishing it from siblings like 'search_docs' or 'list_section_topics' which handle broader queries or lists. It specifies the return format ('chunked content optimized for LLM consumption'), making the purpose explicit and differentiated.
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 usage for retrieving specific topics via identifiers, but does not explicitly state when to use this tool versus alternatives like 'search_docs' for broader queries or 'list_section_topics' for section-based listings. It provides context (fetching by ID/URL) but lacks explicit guidance on exclusions or named alternatives.
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:

[![synergyde-mcp MCP server](https://glama.ai/mcp/servers/h0ck3ystyx/synergyde-mcp/badges/card.svg)](https://glama.ai/mcp/servers/h0ck3ystyx/synergyde-mcp)

Score Badge

Copy to your README.md:

[![synergyde-mcp MCP server](https://glama.ai/mcp/servers/h0ck3ystyx/synergyde-mcp/badges/score.svg)](https://glama.ai/mcp/servers/h0ck3ystyx/synergyde-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/h0ck3ystyx/synergyde-mcp'

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