langchain-relm provides 8 well-structured tools with clear verb-noun naming (relm_describe_schema, relm_search, relm_list, relm_create_*). All tools have descriptions (avg 150 chars, within 10-1024 baseline). Input schemas are explicit Pydantic models with typed fields. However, several parameters lack descriptions (first_name, last_name, phone, linkedin_url in relm_create_contact; domain in relm_create_company), and output schemas are not documented. Error handling is mentioned in docstrings but not formalized. Tool composition is sound, search/list enable discovery before create/update operations. No security issues detected (no secrets in params). Naming is consistent and action-oriented.

• Add descriptions to all parameters in relm_create_contact: 'first_name: Contact's first name.', 'last_name: Contact's last name.', 'phone: Phone number (E.164 format recommended).', 'linkedin_url: LinkedIn profile URL.'
• Document output schemas for all tools. Example for relm_search: 'Returns {contacts: [{id, name, email, ...}], companies: [...], deals: [...], total: int}'. Include field types and which IDs are safe to pass to other tools.
• Add explicit validation error examples to tool descriptions. E.g., 'If stage is invalid, the error response includes valid_options: ["prospecting", "negotiation", "closed"], retry with a valid stage.'
• Enforce mutual exclusivity in relm_create_contact via Pydantic validator or document the server-side check clearly: 'At least one of email, phone, linkedin_url, or company_id must be provided.'
• Document pagination limits: 'relm_list returns up to 100 records per call (limit param capped at 100). For large datasets, use q filter to narrow results or implement pagination via offset/cursor if available.'