Generate concise AI-friendly summaries of GitLab merge requests to review key changes, discussions, and status within limited context windows for quick understanding and decision making.
Apache 2.0
205,128 tools. Last updated 2026-06-15 09:36
"Understanding the Concept of LLM Context" matching MCP tools:
- Assemble minimal token-efficient context for any concept, entity, or file by combining function body, structural summary, domain concepts, and logic cluster into a compact text block for LLM prompt injection.MIT
- Retrieve the neighborhood of a concept in the knowledge graph, showing direct and indirect connections with configurable depth and direction to identify prerequisites and unlock relationships.MIT
- Capture an ARIA accessibility snapshot of a web page as a YAML role tree, enabling LLM-driven page understanding without HTML parsing.
- Retrieve concept lessons, mastery, and history for a specific task statement ID.MIT
- Check if an LLM's answer is grounded in the provided context by verifying factual claims against retrieved documents.Apache 2.0
Matching MCP Servers
- AlicenseBqualityDmaintenanceShare code context with LLMs via MCP or clipboardLast updated4301Apache 2.0
- Alicense-qualityFmaintenanceEnables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.Last updated3MIT
Matching MCP Connectors
Stop re-explaining yourself to Agents. Give it the right context, right when needed.
MCP server for accessing curated awesome list documentation
- Navigate the OMOP vocabulary hierarchy to find ancestor or descendant concepts, supporting phenotype definition and concept set building.MIT
- Retrieve complete concept details, hierarchy, and mappings in a single call. Understand a medical concept's definition, position in classification, and cross-vocabulary links without multiple queries.MIT
- Extract specific numbers and structured data from long documents like press releases or annual reports by providing a URL and a focus. LLM-mediated extraction ensures context-aware results without hallucinations.Apache 2.0
- Screen SEC filers by a specific XBRL concept and fiscal period to compare financial data across all companies.MIT
- Build a knowledge graph from a seed memory using embedding similarity to explore how a concept connects to stored knowledge.MIT
- Modify an LLM's configuration including name, temperature, or max tokens by specifying the LLM ID and the fields to update.MIT
- Retrieve detailed information about an OMOP concept by its numeric concept_id. Get concept name, vocabulary, domain, class, standard status, valid dates, and synonyms.MIT
- Retrieve the summary, facts, and relationships for a specific entity like a person, project, or concept.MIT
- Search GitHub for public proof-of-concept exploit repositories for a CVE, with results sorted by star count to highlight credible exploits.Apache 2.0
- Delete a large language model by its unique ID. Permanently removes the specified LLM from the system.MIT
- Retrieve details of a specific LLM by its UUID. Returns full model information from Anam.MIT
- Check existence of proof-of-concept exploits for a given CVE across GitHub, Exploit-DB, and Nuclei templates.Apache 2.0
- Retrieve the local neighborhood of a concept or symbol, returning nodes and edges to explore connected entities within a repository's knowledge graph.MIT
- Extracts OceanBase documentation context using keywords from user queries, enabling accurate LLM responses by retrieving and integrating relevant information dynamically.Apache 2.0