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Context management tools for coding copilots. Provides tools for managing a local knowledge base, discovering integrations, and preparing context for AI assistants.
Kai MCP server has severe definition quality gaps. Of 25 tools, the input schemas are completely empty or missing for 24 of them (96%). Tool #14 (search_integration_info) is the only tool with a visible, properly-formed schema. All other tools declare Input: {} with no parameters defined. Descriptions exist but are generic (10-80 chars, well below the 50-200 char optimum). No parameter descriptions are visible in the schema definitions. The server also relies on STDIO-only transport, which caps protocol readiness at 50.
Classify the file and extract metadata
Fetch and extract relevant information from an API documentation URL. Returns structured information about endpoints, authentication, etc.
Generate a complete ConnectorManifest from the gathered integration information. Call this after you have collected enough information about the service.
Verify MCP server connectivity
Ask questions about the knowledge base
Remove outdated files from the knowledge base
Discover and generate connector manifests for any integration service
24 of 25 tools (96%) have empty input schemas (Input: {}). No parameters, types, or descriptions are visible. This violates the schema documentation requirement and prevents LLMs from understanding what inputs each tool accepts.
STDIO-only transport. Server uses stdin/stdout JSON-RPC exclusively. This hard-caps protocol readiness at 50 and prevents use with hosted MCP clients (Claude.ai, hosted LLM providers, etc). All production MCP servers must support HTTP or HTTP+SSE.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 39 | 2026-07-28+ | v2 |
Set up a new Kai knowledge base
Categorize and store knowledge files
List available integrations from the registry
Retrieve relevant context for a task
Provide context for a task
Update an existing connector manifest
View usage and cost tracking information
Configure and use an integration
Verify that an integration is correctly configured and can connect
List connectors that are already registered. Use this to check if a connector already exists before creating a new one.
List all files in the knowledge base with summaries and metadata. Use this to decide which files to read.
Parse an OpenAPI/Swagger specification and extract operations, authentication methods, and schemas. Returns structured connector information.
Read the full content of a specific knowledge base file. Use after listing files to read ones relevant to the task/question.
Register the generated connector manifest in the local registry. This makes the connector available for use.
Report files in the knowledge base that contradict the new file being categorized. Use the full path (subdirectory/slug) from the list_knowledge_files output.
Search for API documentation, authentication methods, and endpoint information for a service. Returns relevant documentation snippets.
Select which knowledge base files to surface for this task
Select a framework scaffold when the task involves building an agent, workflow, or LLM-powered application
Incomplete parameter descriptions. Tools like kai_learn, kai_ask, kai_delete have minimal descriptions (30-45 chars) that do not explain what input is expected, prerequisites, or expected behavior.
No error handling guidance visible. Tools are listed with risk levels (WRITE, DESTRUCTIVE, READ_ONLY) but the implementation does not show how errors are communicated back to the agent or what guidance is provided for recovery.
Destructive operations (kai_delete, kai_update_connector, kai_discover_integration, register_connector, select_files_for_context, categorize_file, select_framework) lack confirmation/dry-run patterns. No evidence of confirmation_request or preview mechanisms to prevent accidental data loss.
Tool composition issues. Tools like kai_provide_context and kai_prepare_context appear to do similar things (provide context vs retrieve context), risking LLM confusion about which to call. Distinction is unclear from descriptions alone.
Generic/vague naming for some tools. Tools like kai_learn, kai_ask, kai_use_integration, kai_discover_integration lack specific verb_noun clarity about what action they perform or what state change occurs. kai_learn could mean many things, should it be ingest_knowledge, categorize_knowledge, or store_knowledge?
Output schemas are not documented. While tools 14-19 have visible input schemas, no output schemas are declared. LLMs cannot plan downstream tool calls without knowing what fields are returned.