Borrowing it
Nothing to install: this file belongs to marianosalinas/wrds_mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/marianosalinas/wrds_mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/marianosalinas/wrds_mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/marianosalinas/wrds_mcp/claude-md)<a href="https://agentmods.dev/instructions/marianosalinas/wrds_mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/marianosalinas/wrds_mcp/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/marianosalinas/wrds_mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/marianosalinas/wrds_mcp/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.02452 | $0.02452 |
| Opus 5 | $0.01226 | $0.01226 |
| Sonnet 5 | $0.00490 | $0.00490 |
| Haiku 4.5 | $0.00245 | $0.00245 |
Grade A, and why
wrds_mcp CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wrds-mcp
MCP server providing Claude Code with natural language access to WRDS financial data for credit and equity analysis.
Quick Reference
- Run tests:
python -m pytest tests/ -v - Run server:
wrds-mcporfastmcp run src/wrds_mcp/server.py:mcp - Install dev:
pip install -e ".[dev]"
Architecture
Three-tier design: curated tools handle domain-specific logic, query_wrds provides guarded SQL access for ad-hoc analysis, and catalog tools enable schema discovery.
src/wrds_mcp/
├── server.py # FastMCP entry point, mounts 9 sub-servers
├── db/connection.py # Singleton WRDS connection with retry (3 attempts, exponential backoff)
└── tools/
├── _validation.py # Input validation + DataFrame-to-JSON conversion
├── _schema_docs.py # Static column documentation (~95 WRDS mnemonics)
├── catalog.py # Tier 3: discovery — live catalog, schema introspection, ID resolution (3 tools)
├── query.py # Tier 2: guarded SQL — read-only SELECT with safety validation (1 tool)
├── equity.py # Tier 1: CRSP stock tools — price history, returns, summary (3 tools)
├── bonds.py # Tier 1: TRACE/FISD/bondret — transactions, prices, returns, covenants (6 tools)
├── ratings.py # Tier 1: Credit ratings from bondret + Compustat fallback (2 tools)
├── financials.py # Tier 1: Compustat leverage/coverage/liquidity + composites (6 tools)
├── loans.py # Tier 1: DealScan syndicated loan terms + covenants (2 tools)
├── screening.py # Tier 1: Issuer/bond screening, benchmarks, relative value (4 tools)
└── comps.py # Tier 1: Side-by-side comps table (1 tool)
Three Tiers
- Tier 1 — Curated Tools (25 tools): Pre-built tools with domain logic (auto-routing, TTM calculations, multi-source resolution). Use these first.
- Tier 2 —
query_wrds(1 tool): Guarded SQL escape hatch for any metric not covered by Tier 1. SELECT-only, schema-allowlisted, 10K row limit, 30s timeout. - Tier 3 — Discovery (3 tools):
get_data_catalog(what data exists),get_table_schema(column metadata + docs),resolve_identifier(ticker → gvkey/permno/issuer_id).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 138 lines · 2,452 tokens per session scan A a083c3eabb1c
wrds_mcp CLAUDE.md is an instructions file published in the GitHub repository marianosalinas/wrds_mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 2,452 tokens to every session, about $0.0123 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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