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A FastMCP server providing tools for mathematical operations, web search with content scraping, RAG (Retrieval-Augmented Generation) with Pinecone vector database, intelligent retrieval coordination, SQL database operations, and prompt templates.
This FastMCP server exhibits widespread definition quality issues that would prevent reliable agent use. Of 31 tools, 27 have visible implementations with empty or stub function bodies, no actual logic to return meaningful data. Descriptions are sparse or generic (many under 50 chars, well below the 194-char baseline). Parameters lack detailed descriptions, most specify only the name and type without explaining what values are acceptable, why the parameter exists, or what constraints apply. Output schemas are undocumented (no return-type specs visible). Error handling is absent (no try-catch, no guidance, no recovery hints). The two math tools have reasonable structure, but most database, Pinecone, and RAG tools are non-functional skeletons. This appears to be an early prototype rather than a production-ready server.
Add two numbers together.
GROUP BY operations with SUM, COUNT, AVG, etc.
Track who accessed what data
Complete automatic RAG workflow that handles the entire process from query to response
Generate complex JOIN queries
Generate GDPR, HIPAA compliance reports
Export data to backup files
27 of 31 tools have empty or stub function bodies with no actual implementation. Functions declare parameters but contain only a docstring with no executable code. This prevents the server from returning any meaningful data.
Output schemas are undocumented. No tool declares what structure it returns. Without documented return types, LLMs cannot plan downstream tool chaining or extract expected fields. This violates the baseline that 100% of A+ tools have documented return types.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 46 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 16 | - | v1 |
Create a new Pinecone index (takes 30-60 seconds)
Create a new Pinecone namespace (namespaces are created automatically on first upsert)
Anonymize sensitive data for testing
Debug tool to check DuckDuckGo response format
Delete a Pinecone index
Get table structure, columns, types, constraints
Identify columns with PII/sensitive data
Show foreign key relationships
Suggest indexes for better performance
Intelligent retrieval that automatically determines when and how to search based on query intent
List all Pinecone indexes
List all namespaces in a Pinecone index
Get all tables in database
Multiply two numbers.
Analyze and suggest optimizations
Retrieve documents from Pinecone using vector similarity search
Store documents in Pinecone with embeddings
Explain query execution plan
Scrape the content of a URL
Search across all text columns in a table
Row count, size, indexes
Analyze trends over time
Search the web for information
Search web, scrape content, and provide detailed results
Parameter descriptions are sparse or generic. Parameters like 'tables', 'table_name', 'query', 'regulation' lack detail about expected format, constraints, valid values, and use cases. Baseline is 72 chars per param description; most here are 10-30 chars.
Tool descriptions are generic and short (avg ~35 chars). Baseline is 194 chars. Descriptions lack context for when to use each tool, what it returns, prerequisites, and how it differs from similar tools. Example: 'Analyze and suggest optimizations' vs 'Analyzes table structure and query patterns to suggest indexing improvements (requires read access to table stats). Call after list_tables() to see available tables.'
No error handling or recovery guidance. None of the stubs include try-catch, validation, or error messages. If a tool fails (missing table, invalid regex, network timeout), there is no guidance for what the LLM should do next. Patterns require: 'What to do next' + 'categorize as retryable/user-fixable/fatal' + 'include invalid value and constraint'.
Security concerns with destructive and sensitive tools. data_anonymization (WRITE), delete_index (DESTRUCTIVE), and audit_data_access (reads PII) have no permission checks, confirmation steps, or audit logging. Patterns require: gate destructive tools behind permission checks, support dry-run/confirmation, and log all sensitive access.
Naming ambiguity and weak verb choices. 'smart_search' is vague (smart how?). 'build_join_query' and 'aggregate_data' describe internal mechanics rather than user intent. Better: 'search_table_across_columns', 'execute_join_query', 'compute_aggregates'. Baseline: 90% of A+ tools start with a clear action verb.
No pagination or result limiting. list_tables, list_indexes, list_namespaces have no limit or offset parameters. If the database has thousands of tables, returning all causes context explosion. Baseline: tools returning lists should accept page/offset/limit and return a total count.
Unused/diagnostic tools pollute the set. 'debug_search' is a utility tool for developers, not an agent-facing capability. Production servers should not expose debug/test tools. This wastes LLM reasoning cycles deciding whether to call it.