topai-mcp-suite
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| agentpr_guard_auditA | Audits unified git diffs or Pull Requests for AI-hallucinated NPM/PyPI packages, typosquatting attacks, unauthorized boundary imports, and leaked secrets. Use when: Verifying code changes from AI coding agents (Cursor, Claude Code, Copilot) or reviewing PRs before merging into production repositories. Do not use when: Pruning TypeScript/JavaScript AST syntax or linting container Dockerfiles. Alternative: Use |
| tokenprune_wasm_trimA | Prunes TypeScript and JavaScript source code using AST parsing to extract public interfaces, type signatures, and exports while collapsing method implementations into concise summaries. Saves 50%-70% of LLM context window tokens without losing structural semantic information. Use when: Feeding large codebase files or libraries into LLM prompts for high-level architecture reasoning or planning. Do not use when: Auditing git diffs for security vulnerabilities or linting container configurations. Alternative: Use |
| dockerlean_wasm_auditA | Statically lints and audits Dockerfiles for layer bloat, cache-busting RUN ordering, root user vulnerabilities, missing multi-stage build patterns, and excessive image footprint. Use when: Reviewing container configurations, CI/CD pipeline Dockerfiles, or developer images to minimize image size and security risks. Do not use when: Auditing git diffs or evaluating Terraform infrastructure plans. Alternative: Use |
| cidr_shield_lookupA | Performs sub-millisecond offline Radix Trie Longest Prefix Match (LPM) on any IPv4 address to classify traffic as Datacenter/Cloud (AWS, GCP, Azure, Hetzner), VPN, Tor Exit Node, Public Proxy, or Clean Residential. Use when: Authenticating incoming HTTP requests, preventing bot scraping abuse, or assessing IP risk in auth flows without third-party network latency. Do not use when: Performing DNS lookups, WHOIS domain queries, or analyzing SQL databases. Alternative: Use |
| terracost_blast_evaluateA | Evaluates Terraform plan JSON output to calculate infrastructure blast radius, identify high-risk stateful resource destructions (databases, storage buckets), and compute monthly FinOps cost delta estimations. Use when: Pre-apply CI/CD infrastructure reviews, FinOps budget validation, or assessing destruction risk before applying Terraform changes. Do not use when: Linting container configurations or evaluating test flakiness. Alternative: Use |
| pack3d_wasm_packA | Calculates 3D physical shipping carton packing arrangements for warehouse packages and e-commerce items using First-Fit Decreasing (FFD) and Extreme Point heuristics with full 6-axis orthogonal rotations. Use when: Optimizing warehouse carton selection, freight volumetric weight calculation, and logistics order fulfillment. Do not use when: Auditing code diffs, analyzing Terraform infrastructure, or performing software security reviews. Alternative: Use |
| flaky_quarantine_analyzeA | Analyzes multiple historical JUnit XML test run reports using two-state Markov chain transition matrices to statistically separate genuinely broken tests from nondeterministic flaky tests. Generates quarantine recommendations and test stability manifests. Use when: Triage of intermittent CI/CD pipeline failures, isolating nondeterministic test suites, and calculating test failure probability. Do not use when: Auditing unified git diffs or parsing database dumps. Alternative: Use |
| sqltwin_stream_anonymizeA | Streams and anonymizes PostgreSQL and MySQL database dumps, replacing PII (emails, phone numbers, names, IBANs) with synthetic values using HMAC format-preserving encryption while preserving 100% of foreign key joins, relational referential integrity, and unique constraints. Use when: Preparing staging or developer test database copies from production SQL dumps without exposing real user identities. Do not use when: Parsing financial SEC EDGAR filings or classifying IP addresses. Alternative: Use |
| edgarpulse_parse_filingA | Parses raw SEC Form 4 and Form 8-K XML filings to extract C-suite insider transactions, open-market cluster purchases, vesting events, and calculate quantitative managerial conviction scores. Use when: Tracking corporate executive open-market purchases, insider sentiment, and institutional governance changes in financial workflows. Do not use when: Anonymizing SQL databases or evaluating container layer bloat. Alternative: Use |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Each tool targets a clearly distinct domain: AST pruning, PR/diff security, Dockerfile linting, IP classification, Terraform blast radius, 3D packing, flaky test analysis, SQL anonymization, and SEC filing parsing. Descriptions explicitly state when to use, when not to use, and alternatives, making misselection unlikely despite branded names.
All names use snake_case and end with a clear action verb (trim, audit, lookup, evaluate, pack, analyze, anonymize, parse). The pattern is consistent, but opaque product/mechanism prefixes like tokenprune, agentpr, and edgarpulse reduce predictability from the domain alone.
Nine tools is a well-scoped count for a utility suite, with no apparent filler or redundant operations. Each tool appears to earn its place as a distinct local analyzer.
Each tool provides a self-contained operation for its stated purpose, and the suite covers a broad set of engineering and data tasks. Minor gaps exist around composing outputs or applying remediation, but the read-only analyzer model does not require full CRUD lifecycle coverage.