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kitan23

Dedalus MCP Documentation Server

by kitan23
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
OPENAI_API_KEYYesYour OpenAI API key (required for AI features)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
list_docsB
List all available documentation files

Args:
    directory: Optional subdirectory to list (relative to docs root)

Returns:
    List of document metadata
search_docsA
Search documentation using keyword matching (semantic search ready)

Args:
    query: Search query string
    max_results: Maximum number of results to return
    search_content: Whether to search in document content
    search_titles: Whether to search in document titles

Returns:
    List of matching documents with relevance scores
ask_docsC
Answer questions about documentation using AI

Args:
    question: The question to answer
    context_docs: Optional list of document paths to use as context
    max_context_length: Maximum characters of context to include
    user_id: Optional user identifier for rate limiting

Returns:
    AI-generated answer with sources
index_docsB
Index or re-index all documentation for improved search

Args:
    rebuild: Whether to rebuild the entire index from scratch

Returns:
    Indexing statistics
analyze_docsC
Analyze documentation for specific tasks (foundation for agent handoffs)

Args:
    task: Analysis task (e.g., "find_gaps", "generate_outline", "check_consistency")
    docs: Optional list of specific documents to analyze
    output_format: Output format (summary, detailed, structured)

Returns:
    Analysis results ready for agent handoff

Prompts

Interactive templates invoked by user choice

NameDescription
documentation_query Generate a prompt for querying documentation Args: topic: The topic to query about detail_level: Level of detail (brief, medium, comprehensive) Returns: A formatted prompt for documentation queries

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: analyze_docs performs analysis tasks, ask_docs answers questions via AI, index_docs handles indexing, list_docs enumerates files, and search_docs performs keyword searches. The descriptions reinforce these distinct roles, making tool selection unambiguous for an agent.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case naming (e.g., analyze_docs, ask_docs, index_docs, list_docs, search_docs). This predictable structure enhances readability and usability, with no deviations or mixed conventions across the set.

Tool Count5/5

With 5 tools, this server is well-scoped for documentation management, covering core operations like listing, searching, indexing, analyzing, and querying. Each tool earns its place without redundancy, making the count appropriate for the domain's typical workflows.

Completeness5/5

The tool set provides complete coverage for documentation management, including CRUD-like operations (list, search, index) and advanced AI functionalities (analyze, ask). There are no obvious gaps; agents can perform end-to-end tasks from discovery to analysis without dead ends.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources