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27 practical AI tools (MCP server + CLI) — structured frameworks, data processing, and real computation for writing, analysis, coding, and business tasks.
claude-101 provides 27 tools with generally good naming and decent descriptions, but significant gaps in schema completeness, parameter documentation, and output structure definition. Most tools have clear action-verb names and moderately detailed descriptions (average ~150 chars). However, parameter type definitions are present but inconsistently documented, and output schemas are entirely missing from the visible codebase. Error handling is minimal, tools return JSON but lack recovery guidance or actionable error messages. The server targets a broad set of use cases (writing, analysis, coding, business) with reasonable coverage, but individual tools lack the rigor expected for production agent integration.
Analyze code for quality, security, performance, and style issues.
Analyze CSV or JSON data with statistics, outlier detection, and correlations.
Analyze financial data for margins, growth rates, and trends.
Analyze survey data with per-question stats and NPS calculation.
Build a weighted comparison matrix for competitive analysis.
Draft a customer support response to common issues and inquiries.
Create an Architecture Decision Record (ADR) for documenting technical decisions.
No output schemas documented anywhere in the codebase. Tools return JSON via _json() helper but the structure, fields, types, and nesting of responses are entirely invisible to agents. Agents cannot plan downstream tool calls or extract nested data reliably.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 58 | 2026-07-28+ | v2 |
Create a structured blog post outline with word allocations and SEO fields.
Draft a professional email scaffold with sections and tone guidance.
Evaluate a decision framework using structured analysis and multi-criteria scoring.
Format text for a social media platform with character limits and engagement analysis.
Generate comprehensive test cases for code or features.
Get full details of a specific guide.
List all 24 Claude AI use-case guides.
Extract action items, decisions, and attendees from meeting notes.
Create a structured project plan with phases, milestones, and resource allocation.
Prepare for an interview with structured research, questions, and talking points.
Format, optimize, and analyze SQL queries.
Review legal documents, identify key clauses and potential issues.
Generate API documentation scaffold with endpoints, parameters, and examples.
Generate clean, well-documented code scaffold for a given task.
Create a Product Requirements Document (PRD) template with structured sections.
Create a professional proposal template with sections and formatting guidance.
Generate a technical document template with standard sections.
Search guides by keyword.
Create a story structure with beats, tension curve, and character arcs.
Analyze and extract key sentences from a document.
Parameter descriptions are minimal and lack constraint information. For example, 'tone' param in draft_email lists allowed values inline but does not specify them formally as an enum; no validation rules provided. 'target_words' in draft_blog_post has no bounds (min/max). Agents cannot validate input before submission.
No error handling or recovery guidance in any tool. Tools return JSON with optional 'error' field (e.g. get_guide returns {"error": "Guide {guide_id} not found..."}) but no actionable recovery hint. If a call fails, the agent has no guidance on what to try next (retry, search, ask user, etc.).
No tool annotations present (readOnlyHint, destructiveHint, idempotentHint). All 27 tools are READ_ONLY but this is not declared in the tool registration or schema, it must be inferred from the description. This violates the Agentic Tool Pattern for tool metadata.
Parameter naming inconsistency and ambiguity. 'context' param in draft_email and create_adr is vague, the description clarifies it but a more explicit name like 'email_context' or 'decision_context' would reduce LLM confusion. 'content' in scaffold_tech_doc and structure_story could mean many things (input text? output template?).
No pagination or result-limit declarations. Tools like list_guides, search_guides, analyze_data that return collections do not specify if results are paginated, capped, or unbounded. No 'limit', 'offset', or 'next_cursor' parameters visible.