Policy-aware briefing and memory tools for coding-agent harnesses
Hivelore MCP has 33 tools with consistent naming (verb_noun pattern) and basic parameter schemas. However, descriptions are uniformly terse (10-40 chars), far below the 50-200 char baseline for LLM-optimized tools. Parameter descriptions exist but are minimal. Output schemas are not documented. Error handling and recovery guidance are absent. No tool annotations (readOnlyHint/destructiveHint). The server prioritizes breadth over depth, many tools exist but lack the detail needed for confident LLM selection and execution.
Check code for anti-patterns
Bootstrap and save project configuration
Browse code exports and symbol locations
Semantic search over code symbols
Get policy-aware briefing for files or symbols
Get project context and configuration
Get session recap and summary
Descriptions uniformly terse (10-40 chars). Baseline for LLM-optimized tools is 50-200 chars. Examples: 'Get policy-aware briefing for files or symbols' (47 chars), 'Save a new memory entry' (23 chars). LLMs cannot reliably select tools or understand when to use them vs similar tools (e.g., mem_save vs mem_update vs mem_feedback).
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 51 | 2026-07-28+ | v2 |
Ingest findings from external tools
Approve a memory entry
Find potentially conflicting memories
Delete a memory entry
Show differences between memory versions
Distill memory entries into summaries
Submit feedback on a memory
Get memories for specific files
Get a specific memory entry by ID
List all memory entries
List pending memory entries
Reject a memory entry
Find memories relevant to a query or file
Resolve project context for memories
Save a new memory entry
Search memory entries
End a memory session and save recap
Suggest memory topics based on content
Show memory timeline and history
Record a failed attempt with a memory
Update an existing memory entry
Verify memory entries for correctness
Run pre-commit checks on code
Propose a new sensor for pattern detection
Report friction or issues encountered
Generate test scaffolding for code
No output schemas documented. LLMs cannot plan downstream tool calls or extract required fields (e.g., does mem_search return memory_id, body, tags, anchors?). Breaks tool composition and forces agents to guess response structure.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). LLMs cannot distinguish safe reads from destructive writes. mem_delete, mem_reject, mem_approve, scaffold_test, ingest_findings are marked DESTRUCTIVE/WRITE in metadata but lack protocol-level hints.
No error handling or recovery guidance. Tools lack descriptions of failure modes, retryability, or next steps. E.g., mem_search with no results, should the agent try a broader query, or is the memory system empty? No guidance.
Parameter descriptions minimal or absent. 'tags' appears in 8+ tools with description 'Tags for categorization' or 'Filter by tags', no guidance on format, cardinality, or valid values. 'context' in get_briefing is vague. LLMs cannot validate inputs.