Ultra-lightweight personal AI agent with MCP server capabilities, multi-platform support, and extensible tool ecosystem
MalikClaw provides 2 tools focused on searching/discovering hidden tools. Both tools have descriptions and basic input schemas, but critical quality issues prevent a higher score: (1) tool names lack action verbs, 'tool_search_tool_regex' and 'tool_search_tool_bm25' are repetitive and confusing; a user cannot infer the distinction from the names alone. (2) Descriptions are generic and do not explain when to use one search method over the other, or what 'hidden tools' means. (3) Parameter descriptions lack format/constraint guidance (e.g., regex syntax requirements, query language details). (4) Output schemas are not visible in the source, only noted as 'Returns JSON schemas of discovered tools' without documenting the actual response structure (fields, types, pagination). (5) No error handling guidance, what happens if regex is invalid? If no tools match? (6) The tools are discovery/introspection only, no actual functional tools for agents to invoke, which limits practical utility. The server appears to be a meta-tool for exposing other tools dynamically, but the implementation quality is below production baseline.
Search available hidden tools on-demand using natural language query describing the action you need to perform. Returns JSON schemas of discovered tools.
Search available hidden tools on-demand using a regex pattern. Returns JSON schemas of discovered tools.
Tool names are not action-oriented and repeat 'tool_search_tool' prefix, making them ambiguous. Cannot distinguish regex-based search from BM25 semantic search from the name alone.
Input parameter descriptions are minimal. 'pattern' and 'query' lack format constraints, examples of valid inputs, or error cases. No mention of regex dialect, query syntax, or length limits.
Output schemas are not documented in the source. Code only states 'Returns JSON schemas of discovered tools', no field names, types, pagination structure, or example response visible. LLMs cannot plan follow-up calls without knowing response structure.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | <=2025-11-25 | v2 |
No error handling guidance. What happens if regex is malformed? If no tools match the pattern? If the search backend fails? Tool descriptions do not explain recovery steps.
Tool descriptions do not explain the conceptual difference between regex and BM25 search, or when an agent should prefer one over the other. Generic descriptions reduce LLM's ability to select the right tool.
Server exposes only meta-tools for discovering other tools, not functional domain tools. This limits practical utility, agents can discover tools but have no actual work tools to invoke after discovery.